Mazhar Mughal
email: mazhar.mughal@esc-pau.fr

From Maputo to Malabo: Public Agricultural Spending and Food Security in Africa Charlotte FONTAN SERS

Keywords: JEL Classifications: 011, 055, Q18 public agricultural spending, food security, undernutrition, Africa dépenses publiques agricoles, sécurité alimentaire, sous-nutrition, Afrique

Africa is the world's biggest battleground in the fight against hunger. African governments and the international development community have increasingly focused on finding ways and means to end hunger and ensure the right to food to the continent's burgeoning population.

Public spending on agriculture is one such measure. This study examines the role government spending on agriculture has played in enhancing Africa's state of food security during the past 25 years.

We examine whether this relationship varies over time and space. We explore various aspects of food security and check whether spending on research and development follows the same patterns as the overall public agriculture spending.

We find little evidence of significant beneficial effects of public agricultural spending on food security as a whole. However, food security has improved in countries which spend more on agriculture. Spending on agricultural research and development too has shown a useful impact on Africa's food security. There also exists some evidence in favour of public spending's size and time effects.

We conclude that the commitment by African government in the Maputo Declaration to allocate 10% of public spending to agriculture appears to be pertinent.

Introduction

The potential of agriculture to develop Africa has been increasingly realized in the policy circles. At the 2003 African Union summit in Maputo, Mozambique, African leaders adopted the Comprehensive Africa Agriculture Development Programme (CAADP). The initiative was aimed at promoting agricultural growth, reducing poverty and improving food security in the continent (African Union, 2003). African heads of states committed to allocate at least 10% of the total government expenditure to the agriculture sector within five years. This level of investment was deemed necessary to achieve an average 6% annual agricultural growth rate and attain the Millennium Development Goal (MDG) of eradicating extreme hunger and poverty by 2015 1 . This commitment was reaffirmed in the 2014 Malabo Declaration on Accelerated Agricultural Growth And Transformation for Shared Prosperity And Improved Livelihoods adopted at the African Union summit at Malabo, Equatorial Guinea. The declaration committed to ending hunger and halving poverty in the continent through inclusive agricultural growth by 2025 (African Union, 2014).

Fourteen years since the Maputo declaration, significant progress has been made in allocating higher public funds to agriculture. 11 African countries managed to allocate 10% or more of their budgets to agriculture in any year since 2005, while Ethiopia, Kenya, Mozambique and Sierra Leone achieved 6% agricultural growth in most of these years (World Bank, 2015).

Progress has also been made in reducing hunger from the continent even though a quarter of Sub-Saharan Africa's population is still considered undernourished (FAO et al., 2017). Does this reduction in undernourishment seen in Africa owe in any degree to the greater emphasis laid on higher public spending on agriculture? Is this improvement in food security mainly seen among countries who allocate higher share of their budgets to agriculture? Has the association varied over time and space?

1 For details on United Nations Millennium Development Goals, see : http://www.un.org/millenniumgoals/ Country studies such as [START_REF] Aidoo | Determinants of household food security in the Sekyere-Afram Plains District of Ghana[END_REF], Gezimu Gebre (2012), [START_REF] Matchaya | Estimating effects of Constraints on Food Security in Malawi : Policy Lessons from Regressions Quantiles[END_REF], Magana-Lemus et al. (2016), [START_REF] Muche | Determinants of Household Food Security among Southwest Ethiopia Rural Households[END_REF] and [START_REF] Zakari | Factors Influencing Household Food Security in West Africa: The Case of Southern Niger[END_REF] provide some evidence in this regard.

On the macroeconomic level, FAO suggests public spending allocated to agriculture to be one of the key factors for success in reducing undernourishment and poverty, particularly in the rural areas (FAO, 2012;FAO, IFAD, WFP, 2015). According to FAO, hunger is more prevalent in countries where public agricultural expenditure per worker is lower (FAO, 2012 p. 6).

63% of the population of SubSaharan African countries lives in rural areas and depends to a large extent on agriculture (World Bank, 2015). Farmers often lack access to quality seeds, inputs, technology and insurance against natural catastrophes and crop failures. Production in many countries also suffers from civil conflicts and climatic changes (FAO et al., 2017).

Burundi, Central African Republic and South Sudan, for instance, have seen ongoing social strife during the recent past and have suffered from widespread hunger.

Increase in government funding on agriculture leading to higher agricultural growth can thus play an important role in improving farm output and alleviating poverty and hunger. This can especially happen when this growth increases returns to labour and generates employment for the poor (FAO, IFAD, WFP, 2012, p.28). [START_REF] Benin | Agricultural and rural public spending in Africa: conclusions and implications[END_REF] consider public spending as "perhaps the single most important policy instrument available to governments of most developing countries for promoting growth and equitable distribution."

In this study, we examine the role government spending on agriculture has played in enhancing Africa's level of food security over the past quarter of a century. We study this relationship by analyzing available data for African countries using a panoply of empirical specifications and controlling for various economic, demographic, institutional, climatic and geographical factors that influence the state of food security. We examine the temporal and spatial dimension of this relationship as well as the effect of the size of public spending. We explore various aspects of food security and check whether spending on research and development follows the same patterns as the overall public spending allocated to agriculture.

We find little evidence of a significant overall beneficial effect of public agricultural spending on food security in Africa. However, food security has improved in countries and regions which spend more on agriculture. Spending on agricultural research and development too shows some useful impact on Africa's food security. There also exists some evidence in favour of public spending's size and time effects. The results of the study are robust to use of an array of empirical specifications and econometric techniques.

The study is structured as follows: Section 2 presents the trends in government expenditures allocated to agriculture by African governments and describes the food security situation in the continent. Empirical model and data employed are presented in Section 3, followed by discussion of results in Section 4. Robustness measures are reported in Section 5. Conclusions and policy implications are given in the last section.

Overview of public spending on agriculture and food security in Africa

Public Spending on Agriculture

Throughout the 1980s and the 90s, agriculture remained a low-priority item in the policy agenda of most African countries, as focus was mainly on economic reforms and liberalization of the industrial, finance and other service sectors [START_REF] Yu | Trends and Composition of Public Expenditures: A Global and Regional Perspective[END_REF]. Policy rhetoric shifted during the 2000s with realization of agriculture's potential for leading economic growth and poverty alleviation in Africa. Even though public spending on agriculture still remains low compared with on farm investments [START_REF] Lowder | Who Invests How Much in Agriculture in Lowand Middle-Income Countries? An Empirical Review[END_REF], efforts have since been made to raise agricultural productivity through increased government expenditure accompanied by private sector investments and development assistance from international donors.

Regional organizations across the continent devised joint strategies for promoting agricultural growth through collaboration in information sharing, improvements in physical infrastructure Community ECOWAS is based on the Regional Agricultural Investment Programme, the RAIP (ECOWAS, 2008). The program comprises of four components, the first of which is the «Promotion of strategic projects for food security and food sovereignty‖ 2 . Accordingly, all countries have defined their national agricultural investment plans with a strong focus on improving food security (FAO, 2015) 3 .

Agricultural spending by African governments has improved substantially in absolute terms.

However, the spending still remains inadequate relative to the total expenditure (Table 1).

African countries allocate an average of 6% of their annual budgets to agriculture, a share far below the 10% target set at Maputo, and only a fifth of the African countries have reached the 10% expenditure share target in any year since 2003. Although some countries such as Niger, Burkina Faso, Ethiopia, Malawi and Mali regularly allocated 10% or more of their budgets to agriculture, populous countries like Nigeria, Cameroon and Democratic Republic of the Congo could not regularly allocate even 5% of government funds to agriculture. In similar 2 See for details : http://www.ecowas.int/ecowas-sectors/agriculture/ 3 For Senegal for instance, see ftp://ftp.fao.org/tc/tca/CAADP%20TT/CAADP%20Implementation/CAADP%20Post-Compact/Investment%20Plans/National%20Agricultural%20Investment%20Plans/Senegal%20CAADP%20Post -Compact%20Agricultural%20Sector%20Investment%20Plan.pdf manner, little clear upward trend can be seen in expenditure shares by region, and regional averages have remained below 10% during the entire 1990 -2014 period (Table 1). Likewise, while real per capita public spending on agriculture grew seven-fold in East Asia and the Pacific and four-fold in South Asia, expenditures fell by 25% in Africa south of the Sahara. GDP (1990GDP ( -2014) ) Relative to GDP, spending on agriculture fell in several subregions in Africa during the 1990s but has picked up since the 2000s (figure 1). During the first five years since the launch of CAADP (2003CAADP ( -2008)), expenditures grew by a healthy 7.7% per year at an average (IFPRI, 2017). Growth in agricultural spending halted during the subsequent five years (2008)(2009)(2010)(2011)(2012)(2013) averaging a dismal 1.3% per year. Reasons for this slow growth include the spike in food prices during 2007 and drying up of international aid and private investments in the wake of the 2008 global financial crisis.

The best pace of progress was seen in the region of West Africa where public spending grew four-folds to reach 9.7% of total spending in 2010. In 2003, this region had the lowest investment rates of all African regions. Barring countries in this region, few African countries have yet achieved agricultural production levels reached in the 1960s [START_REF] Badiane | Economic recovery in Africa and its determinants[END_REF].

Although the continent as a whole saw the longest period of agricultural growth since independence and agricultural growth in several countries occasionally reached double digits, the performance of many countries has remained weak. Growth has often been volatile and episodes of negative growth have also been reported. At the subregional level, only East Africa registered a healthy growth in spending relative to GDP in the 2008 to 2014 period, while West Africa even suffered a decline in agricultural GDP. To that extent, the objectives of Maputo declaration have still not been attained for many African countries. Besides, the impact of CAADP implementation on agriculture expenditure is reported to be generally negative while that on land and labour productivity is mixed [START_REF] Benin | Impacts of CAADP on Africa's Agricultural-Led Development[END_REF].

State of food insecurity in Africa

Food security is said to exist when all people at all times have access to sufficient, safe and nutritious food to maintain a healthy and active life (FAO, 1996). The phenomenon integrates four dimensions namely stability, access to food, availability of nutritionally adequate food and biological utilization. These dimensions are proxied by indicators such as average value of food production, road density, evolution of cereal import dependency ratio, prevalence of anemia among children under five years of age or sanitation standards in the country.

Today, SubSaharan Africa and South Asia are the world's two main remaining concentrations of food insecurity. In contrast to Western Asia, Latin America and the Caribbean, progress in combating hunger has been slow in the two regions (FAO, IFAD, WFP, 2015). Prevalence rates of undernourishment in SubSaharan Africa still approach 21% at an average whereas in North Africa, the rates are close to 8% (FAO et al. 2017). 31% of African children in 2016 were estimated to suffer from stunted growth, 7% were wasted, and 37% of the women of reproductive age were anemic (FAO et al., 2017).

Table 2. Undernourishment Trends in Africa

Every fourth undernourished human being in the world comes from Africa south of the Sahara (FAO et al. 2017). The number of malnourished people in the continent has grown from 179 million in the 1999-2001 period to over 205 million in the 2014-16 period. All the five regions in Africa saw an absolute increase in the number of malnourished people during the period. Table 2 shows the numbers and proportion of undernourished population for various sub regions of Africa for the 1999-2001 and 2014-16 periods. Eastern and Central Africa still suffer from widespread undernourishment with prevalence rates of 32 and 24.8% respectively.

Although absolute numbers of people suffering from hunger and undernutrition have increased, relative measures show significant improvement. Several African Countries, particularly those in West Africa carried out institutional reforms and took policy measures during the 2000s (for instance Senegal's Cellule de lutte contre la malnutrition or Rwanda's National Nutrition Policy 2007) in order to enable efficient and effective intervention to combat hunger and malnutrition (Malabo Montpellier Panel, 2017a). Collaborative initiatives such as Regional Strategic Analysis and Knowledge Support System (ReSAKKS) helped evaluate and compare progress of African countries in meeting the Malabo 2025 targets for food security through measures such as stunting and wasting among under-five children, exclusive breastfeeding and anemia in women (Covic and Hendriks, 2016;[START_REF] Haddad | Africa's Progress toward Meeting Current Nutrition Targets[END_REF].

Senegal, Ghana and Rwanda are among the top achievers in terms of improving food security in Africa with over 50% reduction on Global Hunger Index achieved during the 2000 -2016 period4 (Malabo Montpellier Panel, 2017b). Angola, Cameroon, Ethiopia and Togo also obtained over 40% reduction during the period.

In countries of West Africa, improvement in diet quality has accompanied increase in calorie consumption [START_REF] Me-Nsope | Changes in Per Capita Food Availability in West Africa: Implications for Agricultural Market Development (Food Security Collaborative Policy Briefs[END_REF]. Per capita availability of roots and tubers and fruits and vegetables has increased as has the consumption of animal proteins, even though consumption of the latter still remains low by world standards. Part of this diet improvement has come from imported food items such as chicken, Irish potatoes as well as rice. Whereas rice production in West Africa increased from 6.4 million tons in 1992 to over 12 million tons in 2011, imports tripled to almost 10 million from an initial 3.5 million tons in 1992. Africa's average Import Dependency ratio rose from 25.2% in 1990-92 to 42% in 2011-2013. This sharp increase is in part due to the emergence of African middle class with diversified taste for food. Another reason is the national policies during the later part of the twentieth century which favoured cultivation of cash crops for exports at the cost of food and cereals.

Model, methodology and data

Model

As discussed in the previous section, food security is a multi-dimensional phenomenon whose different dimensions are difficult to take into account. Few studies have empirically examined factors that determine the level of food security on the macroeconomic level. In their 2015 report on the state of food security, Food and Agricultural Organization of the United Nations suggest inclusive growth, social protection in the rural areas, well functioning markets, good governance, price stability, agricultural productivity and small farmer income to be among important factors which help alleviate undernourishment in a country (FAO, IFAD, WFP, 2015). Economic growth, which enhances access to food, assets and resources, particularly for the poor and women and allows them to realize their potential, can strongly affect food security. Growth, while a necessary condition for progress in poverty alleviation and hunger reduction especially in the face of an expanding population, is not sufficient if it does not improve the lives of the poor (FAO, IFAD, WFP, 2015). The FAO study shows that the relationship between GDP growth and food security is positive for developing countries as a whole. This association weakens as the country grows richer.

Inflation too has a strong influence on food security. Food accounts for a proportionally higher share of poor households' budgets, and sudden and large increases in food prices hurt them more [START_REF] Bora | Food security and conflict. 2015 international hunger targets: taking stock of uneven progress[END_REF].

Demographic factors too affect food security. Slowing population growth rates and falling infant and child mortality helps improve food security situation by lowering pressures on available resources. The availability and access to food could be undermined in the aftermath of weather or climatic shock such as crop failure, famine, food or water shortage.

In this study, we control for these drivers of food security in order to gauge the relationship between public agricultural spending and food security in Africa. We also control for the continent's spatial dispersion. Africa's five geographical regions: North, Centre, West, East and South differ widely in demographic, climatic, topographical and economic characteristics and thereby present varying food security scenario. In our model, Central Africa is taken as the base region among the five African subregions.

Our baseline model is given as:

Undernutrition_prevalence i,t = f (gdpag i,t , gdpgrowth i,t , inflation i,t , popgrowth i,t , naturalhazard i,t , Caadp i,,t, , northafrica t , eastafrica t , westafrica t , southernafrica t ) (1)
where 'i' represents the corresponding African country and 't' the year of the observation. The proportion of population suffering from undernutrition is taken as the main food security indicator. Caadp is a binary indicator for the year that the corresponding country began participation in the CAADP.

Methodology

The study proceeds in the following steps:

In the first step, the baseline model is estimated using random and fixed-effect panel data techniques. The random-effects model is found to be more appropriate. The model is estimated using different indicators of public agricultural spending: share of agricultural expenditures in the total government budget (the default indicator), logged amount of public agricultural spending, public agricultural spending as a proportion of GDP, per capita public spending on agriculture, public agricultural spending relative to rural population, and per acre public agricultural spending. Alternatively, the model is estimated by including the squared term of the default spending variable.

We also estimate the baseline model regressed on the change in undernutrition levels.

In the second step, we focus on the time dimension by alternately using one, two and threeyear lagged values of public agricultural spending. We interact the spending variable with the caadp time dummy. In addition, we estimate the baseline model for the pre-Maputo and post-Maputo as well as the 2004 -2008 and 2009 -2014 time periods. The first pair of estimations looks for differential impact of public spending on food security before and since the African countries signed the Maputo accord. The second pair divides the post-Maputo declaration period into two five-year subperiods.

In step 3, we take into account possible size effect of public spending by alternately limiting the dataset to above median and top quartile countries in the public agricultural spending distribution. We consider both the relative (with respect to total government spending) and absolute (amount in US dollars) measures of agricultural expenditure.

In step 4, we explore spatial diversity by estimating the baseline model on subsets of countries pertaining to Africa's five regions.

In Step 5, we examine the impact of public agricultural spending on the four aspects of food security namely availability, access, stability and biological utilization. The indicators corresponding to the four aspects include average dietary energy supply adequacy, domestic food price index, domestic food price volatility, and proportion of population using improved sanitation facilities. Health and sanitation conditions may play a significant role in improving households' nutrition situation (DeWalt, 1993;[START_REF] Headey | Developmental Drivers of Nutritional Change: A Cross-Country Analysis[END_REF]. Enhancements in the share of population having access to improved sanitation reflects better hygienic conditions and greater safety from preventable diseases which helps ameliorate nutritional adequacy.

In Step 6, we consider a crucial subcategory of public expenditures on agriculture, namely research and development (R&D). Indicators alternately used in place of the default indicator include logged amount of agricultural R&D spending, share of agricultural R&D in the country's agricultural GDP, per capita spending on agricultural R&D and the number of scientists per 1000 farmers. The first of these indicators is also estimated with lags of one, two and three years to account for delayed impact of R&D spending on the country's food security.

In step 7, we try additional models as a robustness check by alternately including various additional institutional, social, economic and demographic indicators.

Rule of law is taken as an indicator of the country's institutional quality, while intensity of internal conflict and number of terrorist attacks are used to measure the level of violence facing a country. Conflict is a major factor in the recent deterioration in food security situation in Africa (FAO et al., 2017). 60% of undernourished humans live in countries facing major violence or internal strife. The proportion of undernourished population is between 1.4 and 4.4% greater in the 46 countries going through civil conflict.

Another potential determinant of food security is urbanization which has the potential to substantially change the levels of undernutrition in developing countries. Rural households engaged in subsistent farming who are partially or fully self-sufficient become food deficient after migrating to urban slums.

Another factor examined is the domestic food production whose fluctuations can significantly affect vulnerable segments of the population [START_REF] Mughal | Cereal production and food security in South Asia[END_REF].

In the final step, we estimate the baseline model by employing alternative empirical strategies. We include time and country-fixed effects to account for temporal or spatial heterogeneity. Dynamic panel models such as Arellano and Bond, Generalized Least Squares (GLS) and Blundell and Bond System-Generalized Method of Moments (GMM) are also employed to account for potential endogeneity and autocorrelation of residuals.

We also try instrumenting public agricultural spending with quantity of fertilizers, number of tractors and per capita arable land. The three are found to be weak instruments.

Data

The study covers the period from 1991 to 2014. Estimations are carried out on a useable dataset of 670 observations for a maximum of 37 panels. The definitions and sources of the selected variables are given in Table 3 while Table 4 reports summary statistics of the variables.

Public agricultural spending and food securitythe relationship

Table 5 Columns 1 and 2 show fixed-and random-effect panel estimations of the baseline model. The relationship between the share of agricultural expenditures in the total government spending and the prevalence of undernourishment in Africa is found to be insignificant. There is some possibility that this insignificant relationship may be limited to a particular indicator of public spending. To check for this possibility, we carry out the estimations by substituting the indicator for public agricultural expenditure by other relevant indicators, namely the amount of public agricultural spending, public agricultural spending to GDP ratio, per capita public agricultural spending, public agricultural spending per rural population and public agricultural spending per surface area. The first of these indicators is an absolute measure of spending while the others are relative measures.

Results (given in Columns 3 to 7 corroborate the initial findings. The association between public agricultural spending and food insecurity remains invariably insignificant.

Column 8 shows results of model estimated for change in the level of undernourishment. The relationship is again insignificant similar to the level-indicator estimations. Besides, there are no signs of a non linear relationship (Column 9).

Table 5. Food security and public agricultural spending indicators

Public agricultural spending and food securitytime dimension

Table 6 Columns 1 to 3 show estimations with public agricultural spending respectively lagged by one, two and three years. The association of all the three lags with undernutrition prevalence is found to be statistically insignificant suggesting that time horizon of public spending apparently does not play a significant role in improving food security in Africa.

Column 4 shows results of the estimation with the spending variable interacted with dummy for participation in the Comprehensive Africa Agriculture Development Programme (CAADP). While the relationship between spending and undernourishment remains insignificant as before, the interaction term is significant at the 1% significance level with a negative sign indicating that increasing agriculture spending since implementation of CAADP is associated with falling rates of undernutrition levels.

Columns 5 to 8 report results of estimations carried out on the four subperiods (pre-Maputo, post-Maputo, 2004-2008, 2009-2014). The spending variable is found to be insignificant The significant relationship between agriculture spending and undernutrition seen during the 2004 -2008 period points to the possibility of size effect of the agriculture spending.

Table 6. Food security and public agricultural spendingtime dimension

Public agricultural spending and food security -Size effect

Now we examine if the overall insignificant relationship between food security and public spending on agriculture seen so far is applicable on all countries regardless of the size of their budgetary allocations. Countries such as Burkina Faso, Ethiopia, Malawi and Mali allocated substantially high proportion of their annual budgets to agriculture.

Table 7 reports estimations pertaining to subsamples of countries in the above median and top quartile part of the spending distribution. The relationship is found to be insignificant for the default relative indicator (Columns 1 -2) but significant for the absolute indicator with a negative sign (Columns 3 -4). This latter set of results gives tentative support to size effect of the agriculture spending as higher amounts spent on agriculture are found to be associated with lower incidence of undernourishment in Africa.

Table 7. Food security and public agricultural spending -Size effect

Public agricultural spending and food security -Space dimension

Region-wise estimations (shown in Table 8) are again found to be mainly insignificant. The relationship is found to be significantly negative for North Africa, a region which allocates a high share of budget to agriculture. The relationship is insignificant for the two laggard regions of Southern and Central Africa.

Table 8. Food security and public agricultural spending -Regional variation

Public agricultural spending and food security -Dimensions of food security

Next we seek answer to the question as to whether the insignificant relationship between food security and public spending on agriculture is valid across various aspects of food security, or is the relationship limited to a particular dimension? Table 9 reports results of estimations pertaining to four aspects of food security, namely availability, access, stability and biological utilization. The four corresponding dependent variables are average dietary energy supply, food price index, domestic food price volatility and percentage of population with access to improved sanitation. The coefficient for public agricultural spending retains its lack of significance in all the four estimations.

Table 9. Food security and public agricultural spending -dimensions of food security

Public expenditure on agriculture and food security -R&D spending

Finally, we consider the possibility that the effect of agriculture spending may vary by type of public expenditure. We focus on a key spending category, namely agricultural research and development. Columns 1 to 4 of Table 10 show estimations with four indicators of agricultural R&D namely amount of agricultural budget allocated to R&D, R&D spending on agriculture as a share of agricultural GDP, per capita R&D spending and the number of scientists per 1000 farmers.

Both the amount of R&D expenditures (Column 1) and the number of scientists (Column 4) show a significant negative association. This beneficial effect on food security is also visible after one and two years of the R&D spending (Columns 5 -6). The relationship turns insignificant beyond the second year (Column 7).

Table 10. Food security and R&D expenditures

Robustness measures

Public agricultural spending and food securityadditional controls

In addition to the baseline parsimonious model, we estimate a number of models with additional controls. Table 11 Columns 1 to 3 report specifications alternatively including rule of law, conflict intensity and number of terrorist attacks faced by the country. Estimations reported in Columns 4 and 5 allow for the proportion of population living in rural areas and domestic food production. The association between public spending on agriculture and undernutrition invariably remains insignificant regardless of the level of significance of the control variables included.

Table 11. Food security and public agricultural spending -Additional controls

Public agricultural spending and food security -Alternative models

The overall relationship between food security and public expenditure on agriculture has so far been mainly found insignificant. In the following, we carry out various estimations to test the robustness of this finding. We run models to tackle possible issues of heteroscedasticity, serial autocorrelation and endogeneity.

Table 12 Column 1 shows model including time-fixed effects while Column 2 shows estimates including country-fixed effects. These estimations account for the possibility of over-time or cross-sectional variation. Columns 3 to 5 report results of Arellano and Bond, GLS and System-GMM estimations.

In all of these models, the lack of significance of the relationship of interest remains intact.

Table 12. Food security and public agricultural spendingalternative models

Discussion and concluding remarks

Africa is the world's biggest battleground in the fight against hunger. African governments and the international development community have increasingly focused on finding ways and means to end hunger and ensure the right to food to the continent's burgeoning population.

Public spending on agriculture is one of these measures. In this study, we examined the association of expenditure on agriculture allocated by African countries during the past quarter of a century with the progress in combating food insecurity. We employed a number of econometric techniques and carried out a large array of estimations. We fail to find an overall significant beneficial role of these expenditures in the evolution of Africa's overall food security situation. We can be reasonably sure that this lack of significance is not a statistical artifact arising from empirical methodology or an econometric quirk. Moreover, there does not seem to be a time lag beyond which the association with food insecurity turns significant.

This notwithstanding, we find evidence in favour of a size effect of the amount of public agricultural spending. Countries which allocate high amounts of spending to agriculture are found to improve their food security.

Results taken as a whole would suggest that for the majority of African countries, public spending on agriculture over the past quarter of a century has remained below the levels which would allow a diminution of undernutrition. The recommendation to allocate 10% of government spending to agriculture seems pertinent.

We also find evidence for some beneficial effect of R&D spending on agriculture on Africa's food security situation. This highlights the need for better directing the spending towards expenditure items that can reach the segments of population the most in need. In Africa south of the Sahara, R&D has received more than a 40 per cent increase in government funding during the past decade (IFPRI, 2015). The 2015 FAO, African Development Bank Group, ECOWAS report arguments in favour of steering government spending towards R&D and the provision of other public goods (e.g. road network, access to water and electricity) rather than subsidizing or supporting private goods (e.g. agricultural equipment, seeds, fertilizers and other inputs). The mantra is -more public goods fewer subsidies‖ (FAO, ADBG, ECOWAS, 2015). Better targeting of public spending is therefore as important as focusing on increasing existing expenditures.

Here a caveat must be mentioned : the transparency, reliability and quality of data on agricultural expenditures in Africa leaves much to be desired. [START_REF] Mogues | Reconstructing public expenditure data: Use of classification systems to better measure public spending in agriculturea Mozambique case study[END_REF] term the public spending statistics a ‗Black box'. According to the Open Budget Index from the International Budget Partnership 5 , African countries such as Niger, Egypt, Chad and Equatorial Guinea are among the least transparent in the world in terms of budget declarations. Besides, in spite of NEPAD's recommendation to follow Classification of the 5 For detail, see http://survey.internationalbudget.org/#rankings.

Function of Government (COFOG), African countries employ a variety of classifications of agricultural spending [START_REF] Benin | Complying with the Maputo Declaration Target : Trends in Public Agricultural Expenditures and Implications for Pursuit of Optimal Allocation of Public Agricultural Spending[END_REF]Curtis and Adama, 2013;FAO, 2011). This makes comparative analysis cumbersome. Results obtained from the data made available by the SPEED database of IFPRI used in this study should therefore be interpreted with caution. ECOWAS. (2008). Regional Agricultural Policy for West Africa "Make agriculture the lever of regional integration" . 12p. EM-DAT. (2015). The CRED/OFDA International Disaster Database. Université catholique de Louvain-Brussels-Belgium.

World

FAO. (1996). Rome Declaration on World Food Security and World Food Summit Plan of

Action (World Food Summit 13-17 November 1996). Rome: FAO. Note :

FAO. (2012

Column 1 shows results for estimation including the log of the amount of R&D spending. Column 2 shows results for estimation including R&D as a share of GDP. Column 3 shows results for estimation including per capita R&D spending.

Column 4 shows results for estimation including the number of scientists per 1000 farmers. Columns 5 -7 show results for estimation including 1, 2 and 3 lags of the amount (in log) of R&D spending respectively. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

  and research and development (R&D). Examples in this regard are the Common Agricultural Policy for Economic Community of Central African States, the Agricultural Policy of the West African Economic Community (ECOWAP), and the Regional Agricultural Policy of the Southern African Development Community (SADC). As an example, the implementation of the ECOWAP Agricultural Policy adopted in 2005 by the West African Economic

  in three out of four estimations. Only the 2004 -2008 subsample shows a weakly significant association between agriculture spending and undernutrition (Column 7). As mentioned in Section 2.1, budget allocations for agriculture improved in a number of countries after the 2003 Maputo declaration. The fact that much of the world including Africa was enjoying high economic growth also helped. During the 2003-2008 period, annual expenditures by African countries grew by a healthy 7.7% (IFPRI, 2017). The emphasis on agriculture waned in the wake of the 2007 -2008 global financial crisis with annual agriculture expenditure averaging a negative 1.3%.

  Development Report background papers. Covic, N., & Hendriks, S. (2016). Achieiving a nutrition revolution for Africa : The Road to Healthier Diets and Optimal Nutrition (ReSaaks Annual Trends and Outlook Report). IFPRI. Curtis, M., & Adama, D. (2013). What constitutes agriculture spending? Areas of consensus and contention. Action Aid. De Walt, KM. (1993). Nutrition and the commercialization of agriculture: ten years later., 36, 1407-1416.

Table 1 . Public agricultural expenditures as a share of total spending (1990 -2014) Figure 1. Public agricultural expenditures as a share of

 1

Table 3 . Data description

 3

Table 4 . Summary Statistics 4. Findings

 4

Table 1 . Public agricultural expenditures as a share of total spending (%) 1990 -2014 Subregion

 1 ). The State of Food and Agriculture: Investing in Agriculture for a better future. UNICEF, WFP and WHO. (2017). The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security (p. 132). Rome: FAO. FAO, IFAD & WFP. (2012). The State of Food Insecurity in the World Economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition. FAO. FAO, IFAD, & WFP. (2015). The State of Food Insecurity in the World 2015. Meeting the

	Rome: FAO.
	FAO. (2015). Regional Overview of Food Insecurity in Africa, African Food Security
	Prospects Brighter Than Ever. Rome: FAO.
	FAO, African Development Bank Group, & ECOWAS. (2015). Agricultural growth in West
	Africa : market and policy drivers (p. 406). Rome.
	FAO, IFAD,

Table 2 . Undernourishment Trends in Africa Sub Region

 2

		Number of undernourished people	Undernourishmen t prevalence (%)
			(million
			)
		1999-2001	2014-16 1999-2001 2015
	North Africa	9,8	18,6	6,80%	8,30%
	East Africa	112,8	125,8	39,40%	32%
	Middle Africa	35,8	37,6	37.2%	24.8%
	Southern Africa	3,7	4,4	7.1%	7%
	West Africa	36,2	37,3	15.4%	10.6%
	SubSaharan Africa	179,1	205,2	28,2	21,3

Figure 1. Public agricultural expenditures as a share of GDP 1990-2014

  the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.

	agriresearchers	Researchers, total (FTEs per 100,000 farmers)	ASTI
	gdpgrowth	Growth of GDP (constant 2010 US $)	World Bank national accounts data, and OECD National
			Accounts data files.
	inflation	Inflation, consumer prices (annual %)	International Monetary Fund, International Financial
			Statistics and data files.
	popgrowth	Population growth rate (%)	UNICEF, WHO, World Bank, UN DESA Population
			Division
	natural__hazard	Number of disasters associated with food shortage, crop failure or famine	Emdat
	caadp	participation in CAADP (Comprehensive Africa Agriculture Development Programme)	AU
	rule_of_law	Reflects perceptions of World Bank World Development Indicators database
	attacks	number of terrorist attacks in a given year	Global Terrorism Database
	intensity_level	level of conflict intensity measured in terms of battle-related deaths in a given year	UCDP PRIO Armed Conflict Dataset
	rural_pop_share	Proportion of total population living in rural areas	World Bank World Development Indicators database
	foodproductionindex	Food Production Index	FAOSTAT
		Source : SPEED Database (IFPRI, 2017)

Table 4 . Summary Statistics

 4

	VARIABLES

Table 5 . Food security and public agricultural spending

 5 Note : Columns 1 and 2 show random-and fixed-effects specifications of the baseline model with agricultural spending as a share of total spending. Columns 3 -7 show estimations alternately using the amount of public agricultural spending in log, public agricultural spending to GDP, per capita public agricultural spending, public agricultural spending per rural population and public agricultural spending per acre instead of share of agriculture in total public spending. Column 8 shows regression of the differenced under-nourishment indicator. Column 9 shows estimation using squared values of share of agriculture in total spending.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	VARIABLES	under_nourishment	under_nourishment under_nourishment under_nourishment under_nourishment under_nourishment under_nourishment D.under_nourishment under_nourishment
	agshareinspending	-0.161	-0.130						0.0980	0.306
		(0.531)	(0.575)						(0.0603)	(0.938)
	lnagexp			-1.284
				(1.009)
	agexpgdp				0.118
					(0.772)
	agexppc					-0.0383
						(0.0647)
	agexprural						-0.0203
							(0.0278)
	lnagexpland							-1.587
								(1.113)
	sq gdpag_ppp									-0.0714
										(0.108)
	gdpgrowth	-0.223**	-0.225**	-0.213**	-0.229**	-0.228**	-0.229**	-0.210**	-0.0394***	-0.224**
		(0.103)	(0.104)	(0.0956)	(0.104)	(0.102)	(0.103)	(0.0952)	(0.0147)	(0.103)
	inflation	0.000622	0.000615	0.000419	0.000618	0.000610	0.000610	0.000381	5.45e-05***	0.000629
		(0.000406)	(0.000396)	(0.000314)	(0.000408)	(0.000400)	(0.000401)	(0.000304)	(3.54e-06)	(0.000405)
	popgrowth	-0.0884	-0.619	-0.0309	-0.259	-0.237	-0.331	-0.133	0.151	-0.116
		(1.633)	(1.614)	(1.599)	(1.681)	(1.632)	(1.677)	(1.590)	(0.188)	(1.638)
	natural_hazard	-1.774*	-1.831*	-1.536	-1.728	-1.744	-1.738	-1.531	-0.0686	-1.764*
		(1.001)	(1.016)	(1.032)	(1.108)	(1.095)	(1.096)	(1.025)	(0.193)	(1.018)
	caadp	-5.570***	-5.683***	-5.448***	-6.161***	-6.114***	-6.075***	-5.256***	0.0131	-5.626***
		(1.496)	(1.500)	(1.361)	(1.428)	(1.369)	(1.379)	(1.378)	(0.190)	(1.473)
	northafrica	-30.80***		-25.93***	-31.02***	-29.94***	-29.67***	-24.68***	0.933***	-31.14***
		(2.737)		(4.863)	(2.924)	(3.124)	(3.041)	(5.333)	(0.253)	(2.954)
	eastafrica	-2.603		-0.360	-2.459	-2.077	-2.152	2.391	0.404	-2.850
		(6.452)		(7.023)	(6.585)	(6.258)	(6.233)	(7.147)	(0.386)	(6.521)
	westafrica	-13.69***		-12.97***	-13.66***	-13.43***	-13.45***	-10.48***	0.155	-13.85***
		(3.092)		(2.820)	(3.164)	(3.044)	(3.013)	(3.776)	(0.249)	(3.172)
	southernafrica	-7.917		-5.832	-8.165*	-7.177	-7.315	-4.081	0.697	-8.179*
		(4.903)		(5.261)	(4.887)	(5.372)	(5.122)	(5.530)	(0.448)	(4.889)
	Constant	38.01***	27.79***	58.90***	38.14***	38.25***	38.48***	46.53***	-1.171**	37.90***
		(4.406)	(3.748)	(17.18)	(4.488)	(4.458)	(4.567)	(7.619)	(0.473)	(4.381)
	Observations	670	670	657	657	657	657	639	645	670
	R-squared		0.132
	Number of id	37	37	37	37	37	37	36	37	37

Table 6 . Food security and public agricultural spending -time dimension

 6 Columns 1 -3 respectively show public agricultural spending as a proportion of total government spending lagged 1, two and three times. Column 4 shows interaction with the CAADP indicator. Columns 5 and 6 show estimations for the subsamples before and after the 2003 Maputo accord. Columns 7 and 8 show estimations for the2004-2008 and 2009-2014 subperiods.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	VARIABLES	under_nourishment under_nourishment under_nourishment under_nourishment under_nourishment under_nourishment under_nourishment under_nourishment
	l.agshareinspending	-0.0738
		(0.518)
	l2.agshareinspending		-0.0524
			(0.524)
	l3.agshareinspending			0.0547
				(0.472)
	agshareinspending				-0.111	0.737	-0.333	-0.567*	0.00350
					(0.521)	(0.568)	(0.420)	(0.341)	(0.463)
	1.caadp				-2.754
					(1.714)
	caadp * agshareinspending				-1.934***
					(0.643)
	gdpgrowth	-0.196**	-0.139**	-0.0874	-0.223**	-0.0931*	0.0311	-0.0766	-0.0795*
		(0.0856)	(0.0695)	(0.0668)	(0.103)	(0.0536)	(0.110)	(0.0786)	(0.0416)
	inflation	0.000663	0.000712	0.00910***	0.000623	0.000373***	0.0704	-0.0215	-0.0430
		(0.000464)	(0.000504)	(0.000344)	(0.000406)	(0.000115)	(0.0965)	(0.0809)	(0.0339)
	popgrowth	0.179	0.556	1.207	-0.0475	-1.577	3.257	1.816	2.801**
		(1.724)	(1.757)	(1.631)	(1.608)	(1.712)	(2.044)	(1.595)	(1.183)
	natural_hazard	-1.999*	-1.539*	-0.934	-1.596	-1.923*	0.269	1.077**	0.331
		(1.061)	(0.864)	(0.735)	(1.022)	(1.114)	(0.445)	(0.533)	(0.470)
	caadp	-5.410***	-5.486***	-5.407***			-2.823***	-2.538***	-0.737*
		(1.429)	(1.346)	(1.321)			(0.660)	(0.721)	(0.415)
	northafrica	-30.00***	-29.17***	-27.88***	-30.74***	-35.32***	-21.04***	-22.87***	-28.27***
		(2.983)	(3.285)	(3.452)	(2.713)	(2.875)	(4.314)	(4.168)	(1.033)
	eastafrica	-2.329	-2.429	-2.415	-2.595	-5.840	-0.409	0.683	-8.175*
		(6.300)	(6.111)	(5.888)	(6.436)	(7.621)	(5.254)	(6.165)	(4.285)

Table 7 . Food security and public agricultural spending -spending threshold

 7

		(1)	(2)	(3)	(4)
	VARIABLES	under_nourishment under_nourishment under_nourishment under_nourishment
	agshareinspending	-0.557	-0.227
		(0.702)	(0.608)
	lnagexp			-3.610**	-1.156**
				(1.619)	(0.501)
	gdpgrowth	-0.0776	-0.310	-0.246***	-0.0157
		(0.151)	(0.238)	(0.0820)	(0.134)
	inflation	-0.00396	0.0363	0.0366	-0.00490
		(0.0304)	(0.102)	(0.0412)	(0.0191)
	popgrowth	-0.842	-1.816	-0.270	0.775
		(2.290)	(2.449)	(1.732)	(0.929)
	natural_hazard	-3.080***	-1.844**	-1.279*	-0.0399
		(1.051)	(0.823)	(0.693)	(0.910)
	caadp	-6.355***	-4.991***	-4.869***	-4.147***
		(1.626)	(1.661)	(1.570)	(1.302)
	northafrica	-40.48***	-37.82***	-16.52**	-34.07***
		(2.433)	(3.768)	(7.494)	(2.774)
	eastafrica	-9.989	0.970	5.636	-3.984
		(6.370)	(4.916)	(8.023)	(3.407)
	westafrica	-24.51***	-21.49***	-9.584	-26.88***
		(3.773)	(4.042)	(6.576)	(3.195)
	southernafrica	-15.14***	-12.37***	1.853	-13.11*
		(3.589)	(4.686)	(7.577)	(6.819)
	Constant	48.83***	47.79***	97.05***	62.50***
		(4.652)	(7.086)	(28.09)	(11.53)
	Observations	358	175	363	186
	Number of id	30	22	28	19
	Note :
	Column 1 shows estimations for subsample of countries with above median agricultural spending as a proportion of total
	spending. Column 2 shows estimations for top 25% countries in the agricultural spending share distribution. Column 3
	shows estimations for subsample of countries with above median amount of agricultural spending. Column 4 shows
	estimations for top 25% countries in the agricultural spending amount distribution.
	Robust standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Table 8 . Food security and public agricultural spending -space dimension

 8

		(1)	(2)	(3)	(4)	(5)
	VARIABLES	under_nourishment	under_nourishment	under_nourishment	under_nourishment	under_nourishment
	agshareinspending	-1.443**	-0.206	-0.393	7.748***	2.579
		(0.592)	(0.718)	(1.109)	(2.707)	(2.381)
	gdpgrowth	-0.0297	-0.0929	-0.216	-0.430	-0.549***
		(0.0371)	(0.0573)	(0.274)	(0.422)	(0.139)
	inflation	-0.0389***	0.0289	0.00882***	-0.263	0.000233***
		(0.0116)	(0.0432)	(0.000592)	(0.178)	(8.98e-05)
	popgrowth	0.758	-1.561	1.688	12.68***	-5.151**
		(0.648)	(1.334)	(3.093)	(3.083)	(2.394)
	natural_hazard		-1.298	0.0930	-2.655	-5.876***
			(1.439)	(3.359)	(3.325)	(1.748)
	caadp		-6.426***	2.422	-2.503	-1.413
			(1.350)	(3.934)	(2.146)	(2.605)
	Constant	7.230***	27.56***	25.25***	-5.148*	50.29***
		(1.338)	(4.260)	(9.071)	(2.881)	(5.000)
	Observations	74	240	162	128	66
	Number of id	4	13	9	7	4
	Note : Columns 1 to 5 show estimations pertaining to North Africa, West Africa, Southern Africa, East Africa and Central
	Africa respectively.
	Robust standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Table 9 . Food security and public agricultural spending -dimensions of food security

 9

	Note :
	Columns 1 -4 show estimations pertaining to indicators of the four food security dimensions: Average dietary energy supply
	adequacy (availability), Food price index (access), Per capita food production variability (stability), and percentage of
	population with access to improved sanitation facilities (utilization).
	Robust standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Table 10 . Food security and public spending on agriculture R&D

 10

		(1)	(2)	(3)	(4)	(5)	(6)	(7)
	VARIABLES	under_nourishment	under_nourishment	under_nourishment	under_nourishment	under_nourishment	under_nourishment	under_nourishment
	lnagrirespending	-3.348*
		(1.716)
	agriregdp		-0.627
			(0.936)
	agrirepc			-0.283
				(0.458)
	agriresearchers				-0.0506***
					(0.0144)
	L.lnagrirespending					-2.795*
						(1.611)
	L2.lnagrirespending						-2.743*
							(1.647)
	L3.lnagrirespending							-2.555
								(1.727)
	gdpgrowth	-0.0441	-0.0496	-0.0471	-0.0425	-0.0596	-0.0520	0.0117
		(0.0739)	(0.0849)	(0.0807)	(0.0844)	(0.0653)	(0.0588)	(0.0723)
	inflation	-0.0183	-0.0298	-0.0230	-0.0248	-0.00296	0.0268	0.0933*
		(0.0436)	(0.0492)	(0.0487)	(0.0496)	(0.0482)	(0.0568)	(0.0546)
	popgrowth	-1.699	-2.483	-1.863	-2.321	-1.416	-0.805	-0.0924
		(2.034)	(1.967)	(1.985)	(1.984)	(2.136)	(2.101)	(1.775)
	natural_hazard	-0.714	-0.282	-0.440	-0.424	-0.839	-0.869*	-0.451
		(0.647)	(0.743)	(0.640)	(0.629)	(0.628)	(0.496)	(0.573)
	caadp	-4.968***	-5.473***	-5.277***	-5.165***	-5.059***	-4.884***	-4.543***
		(1.152)	(1.186)	(1.115)	(1.162)	(1.110)	(0.970)	(0.925)
	northafrica	-18.06***	-19.16***	-19.11***	-19.44***	-18.56***	-19.52***	-20.52***
		(6.547)	(6.016)	(5.934)	(5.920)	(6.534)	(6.581)	(6.654)
	eastafrica	7.057	2.529	2.652	3.540	5.653	4.378	2.733
		(8.716)	(8.474)	(7.557)	(7.307)	(8.566)	(8.480)	(8.314)
	westafrica	-6.399	-10.07	-8.387	-8.602	-7.340	-8.386	-9.489
		(6.921)	(6.374)	(6.343)	(6.336)	(6.843)	(6.864)	(6.919)
	southernafrica	-0.190	-1.315	-3.038	-3.751	-0.636	-0.798	-1.015
		(8.432)	(8.193)	(7.810)	(7.427)	(8.377)	(8.351)	(8.291)
	Constant	40.73***	37.70***	36.08***	37.57***	39.40***	38.21***	35.74***
		(8.886)	(8.139)	(8.069)	(8.019)	(8.939)	(8.962)	(8.545)
	Observations	497	455	497	498	502	507	507
	Number of id	36	31	36	36	36	36	36

Table 11 . Food security and public agricultural spending -Additional controls

 11 show estimations including indicators for rule of law, terrorist attacks, armed conflict, proportion of rural population, and food production index respectively. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

		(1)	(2)	(3)	(4)	(5)
	VARIABLES	under_nourishment under_nourishment under_nourishment under_nourishment under_nourishmen
	agshareinspending	-0.743	0.0809	-0.658	-0.588	-0.0769
		(0.465)	(0.961)	(1.107)	(0.598)	(0.527)
	rule_of_law	-1.993
		(1.681)
	attacks		0.00785
			(0.00563)
	intensity_level			2.134
				(1.637)
	rural_pop_share				0.732***
					(0.282)
	foodproductionindex					-0.182***
						(0.0424)
	gdpgrowth	-0.0772*	-0.287**	-0.328**	-0.149*	-0.0426
		(0.0412)	(0.123)	(0.149)	(0.0831)	(0.0702)
	inflation	0.00743***	0.000467*	0.00480***	0.000435	0.000734***
		(0.000226)	(0.000256)	(0.000348)	(0.000283)	(0.000210)
	popgrowth	2.386**	1.234	3.377	-1.636	-1.024
		(1.153)	(2.355)	(7.603)	(1.692)	(1.032)
	natural_hazard	-1.062**	-2.822**	-2.452**	-1.242	-0.739
		(0.492)	(1.099)	(0.970)	(1.120)	(0.870)
	caadp	-4.579***	-7.201***	-9.191***	-2.764	1.565
		(0.993)	(1.782)	(3.374)	(1.828)	(1.676)
	northafrica	-23.15***	-31.01***	-28.07***	-23.97***	-32.96***
		(3.316)	(3.578)	(7.068)	(5.540)	(2.702)
	eastafrica	0.748	0.140	5.931	-17.96**	-3.630
		(5.693)	(5.145)	(8.395)	(8.024)	(6.193)
	westafrica	-11.41***	-14.80***	-14.34***	-18.82***	-14.26***
		(3.505)	(3.132)	(4.046)	(4.581)	(3.488)
	southernafrica	-2.926	-8.478	8.467	-15.70***	-7.530
		(4.452)	(6.593)	(5.882)	(6.025)	(5.146)
	Constant	26.52***	36.11***	28.44	0.854	55.98***
		(4.574)	(5.652)	(17.47)	(15.01)	(5.066)
	Observations	432	254	132	670	668
	Number of id	37	30	16	37	37
	Note:
	Columns 1 -5

Table 12 . Food security and public agricultural spending -Alternative econometric techniques

 12 2 show models including time and country-fixed effects respectively. Columns 3 -5 show Arellano and Bond, GLS and System GMM estimations for the baseline specification. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

		(1)	(2)	(3)	(4)	(5)
	VARIABLES	under_nourishment under_nourishment under_nourishment under_nourishment under_nourishme
	agshareinspending	-0.0746	-0.161	0.0529	-0.0746	0.00976
		(0.222)	(0.531)	(0.0534)	(0.364)	(0.0522)
	L.under_nourishment			0.907***		0.890***
				(0.00676)		(0.00563)
	gdpgrowth	-0.140	-0.223**	-0.0444***	-0.140*	-0.0467***
		(0.132)	(0.103)	(0.00580)	(0.0833)	(0.00568)
	inflation	0.000630	0.000622	7.02e-05**	0.000630	7.05e-05**
		(0.000460)	(0.000406)	(3.18e-05)	(0.000431)	(3.16e-05)
	popgrowth	7.364***	-0.0884	0.946***	7.364***	0.835***
		(0.435)	(1.633)	(0.118)	(0.606)	(0.112)
	natural_hazard	-1.841*	-1.774*	-0.0699	-1.841	-0.0988
		(1.046)	(1.001)	(0.101)	(1.344)	(0.0972)
	caadp	-3.399***	-5.570***	-0.414***	-3.399**	-0.463***
		(0.913)	(1.496)	(0.110)	(1.431)	(0.0973)
	northafrica	-24.14***	-30.80***	0	-24.14***	20.06***
		(1.159)	(2.737)	(0)	(1.939)	(0.825)
	eastafrica	-6.062***	-2.603	0	-6.062***	4.904***
		(1.070)	(6.452)	(0)	(1.656)	(0.483)
	westafrica	-17.71***	-13.69***	0	-17.71***	-1.151***
		(1.091)	(3.092)	(0)	(1.533)	(0.352)
	southernafrica	-6.146***	-7.917	0	-6.146***	4.989***
		(1.132)	(4.903)	(0)	(1.628)	(0.535)
	Constant	20.09***	38.01***	-0.181	20.09***	-3.339***
		(0.933)	(4.406)	(0.319)	(1.956)	(0.452)
	Observations	670	670	606	670	645
	Number of year	22
	Number of id		37	36	37	37
	Note:
	Columns 1 and

Global Hunger Index is calculated by IFPRI and captures different dimensions of hunger : undernourishment, child wasting, child stunting and child mortality.


Caroline Lavecchia
email: lavecchi@loria.fr

Kamel Smaïli
email: smaili@loria.fr

David Langlois
email: langlois@loria.fr

Lavecchia Caroline

Smaïli Kamel

Langlois David

Building a bilingual dictionary from movie subtitles based on inter-lingual triggers

Introduction

Training machine translation systems requires a huge quantity of bilingual aligned corpora. Even if this kind of corpora becomes increasingly available, there may be a coverage problem for a specific need. Building bilingual parallel corpora is an important issue in machine translation. Several French-English applications use either the Canadian Hansard corpus or corpora extracted from the proceedings of European Parliament (Koehn, 2005). One way to enrich the existing parallel corpora is to catch the important amount of free available movie subtitles. Several web-sites (http://divxsubtitles.net) provide files used for subtitling movies. This quantity of information may enhance the existing bilingual corpora and enlarges the nowadays-covered areas. Furthermore, subtitles corpora are very attractive due to the used spontaneous language which contains formal, informal and in some movies vulgar words. Consequently, working on parallel movie corpora constitutes a good challenge to go towards realistic translation machine applications. Movies corpora include so many common expressions, hesitations, coarse words,. . . Training translation models on these corpora will lead to spontaneous speech translation machine systems dedicated to a large community. This work could be considered as a first stage towards a real time subtitling machine translation system. For one movie, two subtitle files for two different languages are not necessary aligned because the different files are independently made by different human translators. The raw subtitle corpora cannot be used without pre-processing. In order to make these files convenient for use, it is first necessary to align bilingual versions of the same movie at paragraph, sentence or phrase level. The raw data are difficult to align because subtitles are segmented such that it is easy to read and to write them on screen. Therefore, a sentence may be segmented into several phrases: usually, subtitles are presented on two lines of 32 characters which are readable on six seconds in maximum [START_REF] Vandeghinste | Using a parallel transcript/subtitle corpus for sentence compression[END_REF]. Moreover, the constraint of segmentation applies differently from one language to another because of the language features.

One of the objectives of this paper is to present a method which automatically aligns two subtitle files. This method is based on Dynamic Time Warping (DTW) algorithm.

In the following, we pinpoint the specific features of subtitles and present a measure which helps to align them efficiently. The second objective of this paper is to use this parallel corpus to train the parameters of a machine translation system. In this scope, we propose an original method to construct automatically a translation table without any external knowledge. This objective is handled by inter-lingual triggers which are used to induce a bilingual dictionarywhich overcomes the need of building up a dictionary by hand -and the parameters of the translation table. We describe the idea of inter-lingual triggers, the way to exploit and to make good use of them in order to produce a bilingual dictionary. Then, we describe how to compute the corresponding translation table. Finally, our translation table is evaluated by comparing it to the one achieved by Giza++ [START_REF] Al-Onaizan | Statistical machine translation[END_REF] in an entire translation decoding process supplied by Pharaoh (Koehn, 2004). The experiments show that the obtained translation table is well constructed and is suitable for machine translation. In a tuned use of Pharaoh parameters, our model can outperform the model 3 of Giza++.

Building parallel corpora from movie subtitles

Our objective is to obtain as much pairs of aligned subtitles from movie subtitles as possible. Two subtitles are aligned if they are translations of one another.

Data description

A subtitle file is a set of phrases or words corresponding to: a set of dialogues, a description of an event or a translation of strings displayed on screen (in general destinated to deaf people, or people without skills in the original language). A subtitle is a textual data usually presented at the bottom of the screen. The text could be written in original version or in a foreign language and corresponds to what is being said by an actor Each subtitle is characterized by an identifier, a time frame and a sequence of words. The time frame indicates the interval time the subtitle becomes visible on the screen. The sequence of words corresponds to the literal version of the dialogue or to the reported event. Subtitles as they are presented can not be used directly for alignment because the French and English subtitles do not match. In the example of Fig. 1, the content of the first two subtitles mismatch. In fact the English subtitle begins with a dialogue when the French one does not. Because the movie is American, if any informative message is displayed on the screen, it is thus not necessary to repeat it into the English subtitle file. In the opposite in French the translation is necessary. This kind of difference occurs very frequently and produces gaps between the French and the English subtitles. Several other errors can be present in the subtitle files: omission, insertion of subtitles,... For more details one can refer to [START_REF] Lavecchia | Building parallel corpora from movies[END_REF].

Alignment solutions

The major works aiming at solving the alignment of parallel corpora are based on dynamic programming. These works use a distance measure to evaluate the closeness between corpus segments. A segment can be a paragraph, a sentence or a phrase. The segmentation may be available or calculated automatically as in (Melamed, 1996). Several solutions and different options have been proposed, for more details we can refer to (Moore, 2002;[START_REF] Brown | Aligning sentences in parallel corpora[END_REF]Melamed, 1996;[START_REF] Vandeghinste | Using a parallel transcript/subtitle corpus for sentence compression[END_REF][START_REF] Gale | A program for aligning sentences in bilingual corpora[END_REF]. One can find a comparative study about several methods in [START_REF] Singh | Comparison, selection and use of sentence alignment algorithms for new language pairs[END_REF].

Dynamic Time Warping based on F-measure

Aligning two subtitle files can be considered as a classical problem of dynamic programming. As shown previously, English and French subtitles are asynchronous. To align them, we utilize DTW based on F-measure. This measure is used to calculate the best path between two subtitle files. Intuitively, two subtitles are not aligned if none or only few words of source and target match. This leads to hypothesize that two subtitles do not match if their F-measure is weak. In Fig. 2, each node (e, f ) represents a potential matching point between one English and one French subtitle. A correct path begins at node (0, 0) and ends at node (E, F) where E is the number of English subtitles and F the number of French subtitles. From a node, the following shifts are possible: -vertical progress from (e, f ) to (e, f + 1): the subtitle e corresponds to two consecutive French subtitles -diagonal shift from (e, f ) to (e + 1, f + 1): the shift towards (e + 1, f + 1) means that e + 1, f + 1 are potentially translation one of other. -horizontal transition from (e, f ) to (e + 1, f ): the subtitle f matches with two consecutive English subtitles.

For each node (e, f ), we define a matching score based on the F-measure (F M ) calculated as follows:

S(e, f ) = max    S(e, f -1) + β F m (F M (e, f ) + ε) S(e -1, f -1) + α F m (F M (e, f ) + ε) S(e -1, f ) + λ F m (F M (e, f ) + ε)
α F m , β F m and λ F m are tuned to make the alignment as efficient as possible. These coefficients depend on the value of F M (see section 2.4 for more details). One can notice that the previous formula uses a smoothed F-measure to prevent from a null value. F M is calculated as follows:

F M (e, f ) = 2 × R(e, f ) × P(e, f ) R(e, f ) + P(e, f ) (1)
e is an English subtitle made up of the words e 1 e 2 . . . e |e| and f is a French subtitle

f 1 f 2 . . . f | f | . |e| and | f |
are the sizes of respectively the English subtitle and the French subtitle. The recall R and the precision P are defined by: R(e, f ) = match(e,tr( f

)) |e| P(e, f ) = match(e,tr( f )) | f | (2)
tr( f ) is the list of possible translations for each word in f . tr( f ) is obtained by using a French-English dictionary. The function match is defined by:

match(e,tr( f )) = |e| ∑ i=1 | f | ∑ j=1 δ(e i ,tr( f j ))
(3) tr( f j ) is the set of possible translations of the word f j , given by the French-English dictionary. For example tr(fille) = {girl, daughter}. δ(e i ,tr( f j )) is equal to 1 if the word e i is in the set tr( f j ) and if e i does not already match with a previous French word, e.g:

δ(e i ,tr( f j )) = 1 ⇔ e i ∈ tr( f j ) and ∀k < j, e i / ∈ tr( f k ) (4)
In other cases, δ(e i ,tr( f j )) is set to 0. The second term of the condition allows to impose that an English word can not match with several occurrences in a French subtitle (as in 'you' in Fig. 3). An example of matching is given in Fig. 3.

To make the matching more accurate, we decided to enhance the match function when an orthographic form occurs in both English and French subtitles. This makes, for instance proper names matching without introducing them into the dictionary.

Test Corpora and protocol

Tests have been conducted on a French-English corpus made up of 40 subtitles files (43013 English subtitles and 42306 French subtitles)3 . From each movie, we randomly extracted a subset of English and their French corresponding subtitles. This leads to 1353 English subtitles (corpus T E ), and 1334 subtitles in French (corpus T F ). We aligned by hand the selected subtitles. This leads to 1364 (#A) pairs of subtitles (set A) which constitute our reference corpus. We have more pairs of subtitles than the number of subtitles because several consecutive French subtitles may be aligned with several consecutive English subtitles (because of differences in segmentations strategies [START_REF] Lavecchia | Building parallel corpora from movies[END_REF]): this 'phrase' alignment leads to several pairs. We used a French-English Fig. 3: Illustration of e and f matching dictionary extracted from the XDXF project4 . It contains 41398 entries5 . For the evaluation, we conducted the following procedure:

1. Removing from T E and T F subtitles describing events. 2. Alignment of English and French corpora by using DTW based on F-measure. 3. Deletion of the unuseful pairs: each matching pair for which the F-measure is zero is removed. We recall that if the F-measure is zero, then we hypothesize that the subtitles do not match. 4. Comparison with the reference pairs A

Evaluation

To study the effect of α F M on the efficiency of our method, we tried several values. In the following experiment, α F M varies from 1 (the diagonal is not favored) to 100 and β F M and λ F M are set to 1. Results in terms of recall, precision and F-measure are presented in Table 1. #Tot. is the number of retrieved pairs. #C is the number of correct alignments e.g. the number of pairs present in A. #I indicates the wrong identified pairs. Precision and recall are defined by: The results showed that α F M parameter has a strong effect on the performance. We can notice that F M increases with α F M until 7 and then the value becomes unstable. In order to set definitely the different parameters we have to remind our objective. In fact, we would like to collect as much aligned subtitles pairs as possible without introducing noise. Table 1 shows that this objective is reached when the precision is maximum. In fact, when precision increases, the number of False Positives6 decreases. Considering this objective, we decided to set α F M to 9 in the following experiments. This value leads to 82% of recall and only 94 pairs mismatch. Analyzing results showed that the wrong identified pairs have sometimes a high F-measure. This is due to the weight of small words (prepositions, conjunctions, . . . ). Such words are uniformly present in several subtitles which make the F-measure positive even if the French and English sentences do not match. This is particularly more critical when subtitles are short as illustrated on Table 2. Two potential pairs of alignment get the same F-measure if their constituents have the same length and the same number of matching words. The alignment (E1, F1) is considered correct whereas the second is wrong. Unfortunately, the F-measure refutes this fact. Indeed, the number of words matching in both pairs is the same but the matching in (E1, F2) concerns two small words (language tool word): "à" in French and "to" in English. It is obviously incongruous to let these small words having an important influence on the alignment decision. We can point out that the proper name Wallace (Wallis) is missing in the dictionary. A better dictionary coverage (including this proper name) would achieve a F-measure of 0.44 and would allow the couple (E1, F1) to be a better alignment. To reduce the impact of tool words we modified the formula 6 as follows:

Precision = #C #Tot Recall = #C #A (5)
match(e,tr( f

)) = |e| ∑ i=1 | f | ∑ j=1 γ × δ(e i ,tr( f j )) ( 6
)
Where γ is smaller than 1 when e i or f j are tool words, otherwise γ is set to 1. Results using ( 6) are presented in Table 3. This table shows that assigning less weights to tool words unfortunately does not improve results. The more the weight decreases, the more F-measure, Recall and Precision fall down. Naturally a subtitle is short (between 7 and 10 words) and furthermore it is formed by several tool words, it is henceforth difficult to do without this small words. By examining the subtitles pairs proposed by the automatic alignment (with α F M = 9), we discover that 182 out of 1119 correct aligned pairs matched only because of tool words. By decreasing their weight in the match function, we decreased also the F-measure. This could explain also the last line of Table 3. When we omitted tool words (γ set to 0) we noticed that the number of proposed pairs felt considerably. We remind that in the procedure of alignment, we remove all the pairs (e, f ) for which the F-measure is equal to 0. That is why all the pairs which matched only on tool words disappeared from the alignment, 289 subtitle pairs are concerned by this cut off. With γ = 1, we obtained a precision of 92.3% This result is compet- itive in accordance to the state of art of noisy corpus alignment [START_REF] Singh | Comparison, selection and use of sentence alignment algorithms for new language pairs[END_REF]. These results are very confident and can be used in order to constitute automatic aligned corpora. By launching the developed alignment method with γ = 1 on the total corpus, we detected 4 files among the 40 leading to a very bad alignment. For the following experiments, we decided to discard these 4 files from the corpus. This final corpus contains 32720 subtitle pairs and leads to a precision of 94%.

In this section, we have described a method to align subtitle files. We have evaluated this method by comparing the alignments with a manual reference. In the following, we propose to use this new parallel corpus to estimate the parameters of a subtitling machine translation system.

Translation process

The translation process consists in looking for a E * sentence which is a translation of a given F sentence. This can be done by estimating the probability P(E|F) and by searching E * such that:

E * = arg max E P(E|F) (7)
Using the Bayes rules, Eq. ( 7) is rewritten as:

E * = arg max E P(F|E)P(E) P(F) = arg max E P(F|E)P(E) (8)
P(F|E) is defined by the translation model, and P(E) by the target language model. The translation model may be estimated by defining the involved parameters and by using an iterative process which uses the parallel corpus in order to estimate the parameters. This approach is the one chosen in the Giza++ tool [START_REF] Al-Onaizan | Statistical machine translation[END_REF].

We propose in the following an original method to construct automatically the translation table without any external knowledge. Each couple of words (e, f ) is assigned a probability calculated from inter-lingual triggers. In the following, we describe the idea of inter-lingual triggers, the way to exploit and to make good use of them in order to produce a bilingual dictionary and the translation probabilities P(e| f ). Finally, our translation table is evaluated by comparing it to the one achieved by Giza++ [START_REF] Al-Onaizan | Statistical machine translation[END_REF] in an entire translation decoding process supplied by Pharaoh (Koehn, 2004).

A Brief Remind of Triggers

The concept of triggers has been largely used in statistical language modeling. Triggers improve and generalize the Cache model [START_REF] Kuhn | A cache-based natural language model for speech recognition[END_REF]). The Cache model enhances the probability of a word w i when it occurs in its left context. A trigger model goes further and enhances the probability of a list of words which are correlated to w i [START_REF] Tillmann | Selection criteria for word trigger pairs in language modeling[END_REF]. Triggers between two events x and y are determined by computing Mutual Information given by: MI(x, y) = P(x, y) × log( P(x, y) P(x) × P(y) )

In statistical language modeling, an event x is the occurrence of a word. For each word x, the n best correlated words in terms of Mutual Information are called the triggered Boris Kasparov is a chess champion x is the triggering word. We call a trigger a set made up of a triggering word and its triggered words. See Fig. 4 for examples of triggers.

Triggers allow to estimate the probabilities of words given a history P(w|h), and are combined with n-grams to achieve a better model [START_REF] Tillmann | Word trigger and the EM algorithm[END_REF].

Inter-lingual triggers

We extended the idea of triggers to inter-lingual triggers. A inter-lingual trigger is henceforth a set composed of a word e in a source language, and its best correlated words in a target language f 1 , f 2 , . . . , f n . (see Fig. 5 for examples of inter-lingual triggers). Inter-lingual triggers are determined according to the following formula: Boris Kasparov is a chess champion Boris Kasparov est un champion d' échecs

where f (respectively e) is a French (respectively English) word, and, P(e), P( f ) and P( f , e) are defined as follows: 11) where:

P(X) = N(X) |Corpus| P( f , e) = N( f , e) |Corpus| (
N(X) is the number of sentences where X occurs N(e, f ) is the number of sentence pairs where e and f co-occur |Corpus| is the number of sentence pairs in the training corpus.

For each source word e, we kept the n-best target words f 1 , f 2 , . . . , f n as its triggered words, according to the best MI. This will be written as:

Trig(e) -→ f 1 , f 2 , . . . , f n (12)
Inter-lingual triggers have been also used in [START_REF] Kim | Lexical triggers and latent semantic analysis for cross-lingual language model adaptation[END_REF] to enrich resource deficient languages from those which are considered as potentially important.

Our purpose is to use inter-lingual triggers to generate translation tables for machine translation without using any external knowledge. That is why we compute the cooccurrence between e and f inside each sentence pair of the parallel corpus. Clearly, we would like to retrieve the words in a target language F = f 1 , f 2 , . . . , f n which are correlated to a word e in a source language. Among the set F, we hope to find a subset T which is made up only by the translations of e. We compute inter-lingual triggers on a subset of the subtitle corpus described in section 2 (training corpus statistics are given in Table 4). Table 5 illustrates some examples of the obtained English-French triggers, whereas Table 6 gives some French-English triggers. The third column indicates the Mutual Information associated to each couple (trigger and triggered words).

A qualitative analysis showed that our method leads to remarkable inter-lingual triggers where the triggered words could be considered as potential translations of the trigger or very close in terms of meaning.

Using inter-lingual triggers for building a bilingual dictionary

Our first goal is to provide automatically a bilingual dictionary in multiple languages from inter-lingual triggers. The translations of a source word e are obtained by selecting all the target triggered words f 1 , f 2 , . . . , f n which trigger the source word e as illustrated in Fig. 6. Namely, an entry in a dictionary D is defined as:

e : f 1 , f 2 , . . . , f n ∈ D ⇔ ∀ j ∈ [1..n], e ∈ Trig( f j ) and f j ∈ Trig(e) (13)
In this way, we can build French-English and English-French dictionaries. Table 7 gives a view of the obtained French-English dictionary (see section 4.1 for an evaluation of this dictionary). We called the French-English dictionary Trig-Dic-Reverse because of the membership propriety applied in both English-French and French-English directions.

Using inter-lingual triggers to estimate a translation table

To achieve a translation table using inter-lingual triggers, we assign to each potential word's translation a probability calculated from MI such as:

∀ f , e i ∈ PT ( f ) P(e i / f ) = MI(e i , f ) ∑ e∈PT ( f ) MI(e, f ) (14)
where PT ( f ) is the potential translations of f . We estimate several different translation tables:

-The translation table Trig-Dic is directly calculated from Trig-Dic-Reverse with n = 10 generated in section 3.3. Each potential translation respects the constraint (13). -In the translation table Trig-5, we kept as potential translations of a French word f its 5 best triggered words without applying (13). -In the translation table Trig-10, we kept as potential translations of a French word f its 10 best triggered words without applying (13). -In the translation table Trig-20, we kept as potential translations of a French word f its 20 best triggered words without applying (13).

In this section, we first evaluate our dictionary Trig-Dic-Reverse (see section 3.3) by comparing it with two existing dictionaries. Then, we evaluate the different translation tables produced in section 3.4 by integrating them into an entire decoding process supplied by the Pharaoh decoder (Koehn, 2004).

Evaluation of our French-English dictionary

To evaluate the pertinence of our dictionary (Trig-Dic-Reverse with n = 5), we compared it with two dictionaries: one distributed by ELRA7 and a free downloaded one8 . The comparison is only done on the French-English side. To make the evaluation relevant, we compare only words which exist in Trig-Dic-Reverse and in the two other dictionaries. Our dictionary share 6098 words with the ELRA dictionary and 6228 with the Internet dictionary. The evaluation in terms of recall is presented in Table 8. The results show that if we consider only the translation given in first position, the recall is 16.04%, and if we consider the results without taking care about the rank, the recall reaches 73.74% with ELRA dictionary reference and 71.11% with the Internet dictionary reference. If we consider the ELRA dictionary as a reference, we can say that our algorithm finds out the pertinent translation of a word in 74% of cases. In a first analysis, we can consider that our algorithm has a failure rate of 36%. A deeper analysis contradicts this assertion. In fact, the failure rate can be explained as follows:

-Into Trig-Dic-Reverse we kept only the first five best translations.

-When a potential translation in Trig-Dic-Reverse does not exist in the ELRA dictionary, we notice that frequently the one we propose is correct and sometimes is very close to the meaning. -In some cases, the translation proposed by ELRA is less commonly used than ours as shown in Table 9. Then even if the translation we propose is correct, it is not counted as correct.

To sum up we can say that the results obtained are very interesting and the recall is probably better than 74%. We have to compare Trig-Dic-Reverse to a better reference (a hand-constructed one) to have a precise evaluation.

Translation decoding with inter-lingual triggers

In order to evaluate the real contribution of our method, we have to integrate the retrieved translation tables into an entire decoding translation process supplied by Pharaoh9 (Koehn, 2004). In a first experiment, we use the Trig-Dic translation table generated in section 3.4: each word of source and target language gets 10 potential translations. For each potential translation a probability based on MI is associated. The translation probability for other vocabulary words is set to 0. The decoding process has been conducted on a development and a test corpus (Table 10). Translation results in terms of Bleu (Papineni and al., 2001) are given in Table 11. The performance is compared to the one obtained with Giza++ dictionary using the IBM Model 3 (Brown and al., 1993). Table 11 shows that using the 20 best triggers leads to less powerful results. This is probably due to the nature of subtitles, they are in fact short. Consequently, it is too difficult to find 20 target words which are correlated to a source word within a subtitle. The decoding results based on our method is similar to the one achieved by Giza++ and the best result is obtained with Trig-10. Furthermore, Giza++ trains IBM models 1, 2 and 3 in several iterations to outperform slightly our model. Table 12 shows that until the fifth iteration of IBM2, Giza++ does not outperform Trig-10 whereas Trig-10 needs only one iteration. To improve results, we optimize the Pharaoh parameters for all the decoders (Trig-Dic-10, Trig-Dic and Giza++). Table 13 presents the performance with tuned parameters. In this table, tm, lm, dm and w are respectively the decoding weights for the translation model, the language model, the distortion model and the word penalty. With optimal parameters of Trig-10, we outperform Giza++ by 10,9% whereas with the optimal parameters of Giza++, our model is 6% worse. This orientation is maintained and emphasized

Table 13: Optimization of Pharaoh parameters for Trig-10, Trig-Dic and Giza++ decoders tm lm dm w Bleu-Trig Bleu-Giza++ Trig-10 0.9 0.4 0.4 0 0.1227 0.1105 Trig-Dic 0.9 0.4 0.3 -2 0.1184 0.1094 Giza++ 0.7 0.6 0.5 -2 0.1157 0.1220 on the test corpus (see Table 14). In an optimal use of Trig-10, this one outperforms Giza++ by 8,1% whereas Giza++, in an optimal use, does better than Trig-10 by only 1,2%. Furthermore, the best Bleu score for the translations obtained with the Giza++ translation table is 0.1176 whereas the best Bleu score for the translations obtained with the Trig-10 translation table is 0.1202. In other terms, our method outperforms slightly IBM model 3 by 2.2%.

Table 14: Decoder evaluation with optimal parameters for Pharaoh on the test corpus tm lm dm w Trig-10 Giza++ 0.9 0.4 0.4 0 0.1202 0.1119 0.7 0.6 0.5 -2 0.1161 0.1176

Conclusion and future work

In this paper, we presented a full process of translation, starting at the alignment process and ending at translation decoding. We built a movie subtitle parallel corpora of 32720 aligned pairs with a precision of 94% compared to a manual alignment. Then, this material has been used to construct a dictionary based on inter-lingual triggers.

For each word (French or English) a list of its target corresponding triggers has been proposed. An entry of a bilingual dictionary is made up of a source word and its best target triggers. The obtained dictionary is relevant and first results in an entire decoding process showed that on a test corpus with an optimal set of parameters of Pharaoh, our method outperforms Giza++ by 8,1%. With the optimal parameters of Giza++, this one over-pass our method by only 1,2% . Furthermore, the best Bleu score for the translations obtained with the translation table Trig-10 is 2.2% better than the best Bleu score of Giza++ translation table. Our results are very encouraging and efforts are done in order to improve our model by using phrases in the translation decoding process. The idea of using cross lingual triggers seems to be very attractive, it can be used in several areas in machine translation. For instance, they could be used as a confident measure.

Several other utilizations of this method have been imaginated and are under-work in our research group.

Fig. 1 :

 1 Fig. 1: Source and target movie subtitles

  Fig. 2: Dynamic alignment for subtitles

Fig. 4 :

 4 Fig. 4: Examples of triggers

Fig. 5 :

 5 Fig. 5: Examples of inter-lingual triggers

  Fig. 6: Illustration of dictionary designing

Table 1 :

 1 Performance depending on α F M parameter

	α F M #C #I #Tot. Rec. Prec. Fm.	α F M #C #I #Tot. Rec. Prec. Fm.
	1 1063 842 1905 0.779 0.558 0.650	7 1119 97 1216 0.820 0.920 0.867
	2 1124 213 1337 0.824 0.841 0.832	8 1118 96 1214 0.820 0.921 0.867
	3 1124 114 1238 0.824 0.908 0.864	9 1119 94 1213 0.820 0.923 0.868
	4 1121 99 1220 0.822 0.919 0.868	10 1118 94 1212 0.820 0.922 0.868
	5 1121 98 1219 0.822 0.920 0.868	20 1116 93 1209 0.818 0.923 0.867
	6 1120 97 1217 0.821 0.920 0.868	100 1114 92 1206 0.817 0.923 0.867

Table 2 :

 2 Illustration of mismatching due to small words

		E1 : Wallis hold on to this E1 : Wallis hold on to this
		F1 : Wallace tiens moi cela F2 : Ulrich pense	à
	N(e) 5	5
	N(f) 4	3
	match 1	1
	Prec. 1/4	1/3
	Rec. 1/5	1/5
	F M	0.22	0.23

Table 3 :

 3 Impact of reducing the tool words' weight

	γ #C #I #Tot Rec. Prec. F M	γ #C #I #Tot. Rec. Prec. Fm.
	1.0 1119 94 1213 0.820 0.923 0.868	0.4 1056 171 1227 0.774 0.861 0.815
	0.9 1097 134 1231 0.804 0.891 0.845	0.3 1044 189 1233 0.765 0.847 0.804
	0.8 1097 134 1231 0.804 0.891 0.845	0.2 1040 192 1232 0.762 0.844 0.801
	0.7 1097 134 1231 0.804 0.891 0.845	0.1 1039 194 1233 0.762 0.843 0.800
	0.6 1097 133 1230 0.804 0.892 0.846	0.0 869 55 951 0.657 0.942 0.774
	0.5 1097 133 1230 0.804 0.892 0.846

Table 4 :

 4 Training Corpus statistics

		French	English
	Training Subtitles	27523
	Words	191185	205785
	Vocabulary 14655	11718
	Singletons 7066	5400

Table 5 :

 5 Examples of French words triggered by English words

	English trig-	French	trig-	MI × 10 -4
	ger word	gered word
		pomme		9.32
	Apple	sorbet		3.21
		fruits		2.81
		soldat		11.93
	Soldier	combattez		2.88
		soldats		2.17
		chocolat		64.06
	Chocolate	chocolaté		7.04
		tablette		6.49
		mesdames		23.13
	Ladies	messieurs		20.27
		dames		4.92
		boulot		35.79
	Job	travail		34.02
		emploi		8.08

Table 6 :

 6 Examples of English words triggered by French words

	French trigger	English	trig-	MI × 10 -4
	word	gered word
		apple		9.32
	Pomme	baked		2.96
		beef		2.96
		soldier		11.93
	Soldat	discovers		3.06
		fighting		1.96
		chocolate		64.06
	Chocolat	candy		7.23
		eat		2.81
		ladies		23.13
	Mesdames	gentlemen		15.78
		belt		4.24
		job		34.02
	Travail	work		30.88
		done		7.58

Table 7 :

 7 A selection of few entries of French-English dictionary

	French word Potential translations
	Demande	ask wonder request
	Jamais	never ever	seen
	Mort	dead death died
	Maisons houses homes buy
	Pomme	apple baked beef

Table 8 :

 8 Results in terms of recall

	Rank1 Rank5
	ELRA 16.04 73.74
	Internet 13.08 71.11

Table 9 :

 9 Comparison between ELRA and Trig-Dic-Reverse dictionaries

	Word ELRA	Trig-Dic-Reverse
	chevaux horseflesh horses, breed, turbo
	chimère bubble chimera, monster, virus

Table 10 :

 10 Development and test Corpus statistics

		French	English
	Dev Subtitles	1959
	Words	13598	14739
	Test Subtitles	3858
	Words	22195	24729
	Table 11: Evaluation of automatic translations using Bleu
	Method Giza++ Trig-5 Trig-10 Trig-20 Trig-Dic
	Bleu 0.121 0.113 0.114 0.072 0.113

Table 12 :

 12 Bleu Evolution

	Model-Iteration M1-it1 M1-it5 M2-it1 M2-it5 M3-it5 Trig-10
	Bleu	0.075 0.096 0.097 0.099 0.121 0.114

extracted from the web-site http://divxsubtitles.net

http://xdxf.revdanica.com/

Archive filename: comn sdict05 French-English.tar.bz2

the number of incorrect alignments

M0033-3 SCI-FRAN-EURADIC which contains 70832 entries

http://xdxf.revdanica.com/down/index.php which contains 41398 entries

The target language model is a trigram model (Good-Turing smoothing, cutoff set to 7 for bigrams and trigrams). The decoding weights are set to: 1 for language model, 1 for translation model, 0 for word penalty, and 1 for distortion model. Decoding is with reordering.