Ronny Meyer Inalco

The noncausal/causal alternation in Muher (Ethiosemitic)

Keywords: causative, complex predicates, Ethiosemitic, Gunnän Gurage, impersonal construction, mediopassive, Muher, nonconcatenative morphology causatif, construction impersonnelle, éthiosémitique, Gunnän Gurage, muher, morphologie non caténative, médiopassif, prédicats complexes

This paper is concerned with noncausal/causal verb pairs in Muher -a little-known South Ethiosemitic language of the so-called Gunnän Gurage sub-group in Ethiopia. Muher is a morphologically complex language, characterized by nonconcatenative verb morphology and the use of prefixes in combination with template changes to derive verbs. The study is based on a dataset of the Muher equivalents for the verb pairs in Haspelmath (1993). Muher uses several strategies to mark the noncausal/causal alternation, whereby directed pairs dominate. Within these pairs, a causal verb is more often derived than a noncausal verb. Non-directed pairs, i.e., equipollent, labile and suppletive verb pairs, are also attested, but are less frequent. Moreover, for five of the original items in the list, Muher has no pairs, i.e., one verb only occurs in the noncausal form while four verbs lack a noncausal counterpart. These four verbs, as well as most of the other verbs in the list with a human subject, make use of a morphosyntactic construction, the impersonal, to express an event in which the patientive object is foregrounded.

Introduction

Over the last few decades several typological works have been published showing that languages use various formal devices to mark the noncausal/causal alternation in semantically defined verb pairs (e.g. [START_REF] Haspelmath | More on the typology of inchoative/causative verb alternations[END_REF][START_REF] Nichols | Transitivizing and detransitivizing languages[END_REF][START_REF] Haspelmath | Coding causal-noncausal verb alternations: A form-frequency correspondence explanation[END_REF][START_REF] Grünthal | Transitivizing-detransitivizing typology and language family history[END_REF]; Creissels this volume). According to Haspelmath (1993: 90), the two verbs of such a pair denote the same verbal event which typically contains a patient participant, but the agent participant occurs only with the causal verb, while its noncausal counterpart lacks an overt instigator of the verbal event.

The formal relationship of the two verbs in a pair is not limited to valence-changing derivation, but also includes other types, like lexical suppletion, auxiliary change, conjugation change, etc. For further details, see the Introduction to this volume, where the guest editors provide a definition of the noncausal/causal alternation, present an overview of the typological literature, and give a detailed overview of the correspondence typologies.

Studies of the noncausal/causal alternation in Ethiosemitic languages are scarce. Even if grammars of Ethiosemitic languages may contain a detailed description of valence-changing derivations, other mechanisms for the noncausal/causal alternation have so far not attracted much attention. The only exception is Amharic, for which a 31-item list of noncausal/causal pairs including an analysis of their formal relationships is available [START_REF] Wakasa | The world atlas of transitivity pairs[END_REF].

Based on the set of noncausal/causal meanings presented in [START_REF] Haspelmath | More on the typology of inchoative/causative verb alternations[END_REF], this paper describes the alternations underlying noncausal/causal verb pairs in Muher (endonym: mʷähärɨɲɲa; Glottocode: muher1234; the ISO 639-3 code sgw subsumes Muher under Sebat Bet Gurage). 1 Muher is an Ethiosemitic language spoken in the Gurage Zone in central Ethiopia. An official number of Muher speakers does not exist because the last population and housing census of Ethiopia from 2007 provides only a general figure in which, with the exception of Silt'e (also comprising Wolane), all Gurage varieties are lumped together. About 1.48 million L1 Gurage speakers live in Ethiopia (CSA 2010a: 91), of whom 1.06 million are living in the Gurage Zone (CSA 2010b: 166). Muher is mainly spoken in the Muher and Aklil District in which 87,800 people are living (CSA 2010b: 7). Assuming that most of them speak Muher as their L1, the total number of Muher speakers can be estimated as being between 80,000 and 90,000. Only few of them speak the ädi-bet variety, which is mainly found in and around the village of Mäqorqor, and some villages near Wolkite, such as Tattessa, in which according to my estimation approximately 800 ädi-bet speakers are living.

Genetically, Ethiosemitic is part of Western Semitic (see Huehnergard & Pat-El 2019: 7-9). The exact number of Ethiosemitic languages is still a matter of discussion; for a summary of the thirty or so varieties, see Hudson (2013: 7-20). Despite several contestations, e.g. [START_REF] Goldenberg | The Semitic languages of Ethiopia and their classification[END_REF] and [START_REF] Hudson | North and South Ethiopian Semitic[END_REF]2013), the most comprehensive classification of Ethiosemitic based on morphological innovations still remains Hetzron (1972: 119;1977: 15-24). According to him, Ethiosemitic contains three main branches: North Ethiosemitic, Transversal South Ethiosemitic, and Outer South Ethiosemitic (Figure 1). The symbol † is used to indicate extinct languages.

1. Based on SIL's use of the term, Sebat Bet Gurage consists of branches i-iv of the Gunnän Gurage sub-group in Figure 1.

Figure 1 -Main genetic groups within Ethiosemitic

With the exception of Gafat, Outer South Ethiosemitic languages are grouped together as Gunnän Gurage [START_REF] Hetzron | The Gunnän-Gurage languages[END_REF] 3), as opposed to the genetically rather distant Eastern Gurage (Meyer 2011a(Meyer : 1221(Meyer -1224)). Within Gunnän Gurage, Kistane, Dobbi, Galila and Muher form a special unit known as Northern Gurage (Hetzron 1977: 23-24).

Muher, like most other Gunnän Gurage languages, typically serves as medium for oral communication and has only sporadically been used for writing. The majority of the Gurage, including the Muher, are bi-or multilingual, speaking more than one Gurage variety beside Amharic (Meyer 2011a(Meyer : 1223(Meyer -1224). This has a strong impact on language use of Gurage speakers resulting in various language change and language shift processes.

Muher is a fairly well described language. Besides an extensive wordlist with about 2000 entries (Leslau 1979a: 831-960), glossed and annotated text collections (Leslau 1981: 53-199;Hetzron 1977: 167-184), two grammar sketches (Leslau 1981: 6-50;[START_REF] Meyer | Gurage (Muher)[END_REF], and a comprehensive descriptive grammar (Aregawi 2018), there are a number of specialized grammatical studies on the language (for an overview, see Meyer 2011a).

The data presented in this paper mainly stem from my own fieldwork on Muher with additions from Leslau (1979a). Since 1998, I have been conducting fieldwork on the ädi-bet variety of Muher in the multilingual speech community living in Tattessa, a small village 6 km to the east of Wolkite, the administrative center of the Gurage Zone. Originally, the ädi-bet variety is spoken in the village of Mäqorqor, from where it expanded to several enclaves in the Gurage Zone (Meyer 2010: 161-162). Today, the Muher of Tattessa can be considered heritage speakers of ädi-bet as most of them use Amharic as the dominant language, in addition to other Gurage languages, in particular Ezha. The heritage status of the language affected my interactions with consultants and this should be borne in mind in the analysis that follows.

Grammatical background

As is typical for a Semitic language, the verb morphology of Muher is nonlinear. An inflected verb consists of three components: a verbal root (i.e. a sequence of consonantal or vocalic radicals) encoding the general semantics of the verb, inflectional templates for grammatical aspect and mood, and partly also polarity (as a grammatical category of affirmative vs. negative), as well as subject affixes (see Meyer 2014; 2016a for further details). Figure 2 shows the three stages of the formation of the simplex stem for the root √ k' 1 -b 2 -r 3 'plant; bury' 2 with a 3sg.m subject. First, in stage (i) the lexical root interdigitates with the templates to form the verb bases. 3 Then, in stage (ii) these bases obligatorily combine with a subject marker to form an inflected verb. With regard to the negative perfective and jussive, the inflected verbs already represent main clause verbs, whereas the verbs in the affirmative perfective and the imperfective are unmarked for their syntactic function. They still have to combine either with a clausal status marker, stage (iii), or with a subordinating element (for clausal status marking, see Meyer 2014: 242-252). 4 The basic conjugations found in each Ethiosemitic language are perfective, imperfective 5 and jussive/imperative 6 . A few languages have additional templates for the converb and the verbal noun (or nominal infinitive). Muher does not have a dedicated converb template, but forms pseudo-converbs from inflected verbs. The verbal noun is derived from the jussive template. In addition to the negative prefix, several Gurage languages also distinguish between affirmative and negative perfective by distinct templates (Meyer 2014: 236). In Muher, these templates differ in the second root consonant, which is geminate in the affirmative perfective and single in the negative.

In addition to the templates, three lexical verb classes, called verb type, have to be distinguished, as they determine the vocalization and gemination patterns of the templates. Type A is the basic or unmarked root. Type B is characterized by gemination of the second root consonant in all conjugations and/or palatalization of either the initial root consonant or the vowel following it (yielding *ä>e, *a>ä). Type C is marked by the vowel a following the initial root consonant (for the main verb types in Ethiosemitic, see Meyer 2016a: 169).

As a result, the stems of a verbal root in Muher are not represented by a single inflected verb form but by the combination of the templates for the three basic conjugations (perfective, imperfective and jussive). In other words, the four templates in Figure 2 represent together the simplex stem of the root √ k' 1 -b 2 -r 3 'bury', whereas the three templates in Figure 3 represent the derived mediopassive stem of this root.

Verbs cross-reference definite primary objects by a dedicated suffix set (1a), which is relatively complex in Muher (and other Gunnän Gurage languages) due to a large number of allomorphs (Meyer 2019: 240). Indefinite objects are never indexed, as shown in (1b).

2. The symbol √ stands for a lexical root which typically consists of three consonants whose order is indicated by subscript numbers. In templates, any consonantal and vocalic radical is represented by the symbol C followed by a subscript number to indicate their position in the root. The sequences CC 1 and CC 2 stand for geminate consonants. 3. In postvocalic position, the singleton ejective k' regularly debuccalizes to the glottal stop ʔ (Meyer 2019: 230). 4. Note that the citation form of Semitic verbs is the 3sg.m affirmative perfective, which in Muher ends with the clausal status marker -m. In this paper, verbs are cited in this form in the body of the text, and are glossed with the English infinitive. In numbered language examples, however, the English glosses represent their actual contextual meaning. 5. The imperfective is used in nonpast and past contexts, in the latter followed by the past auxiliary bannä in main clauses (for further details on Muher, see Meyer 2019: 248;and in a wider Ethiosemitic context, see Meyer 2016a: 199-217;2016b: 199-203). In Figure 2, only the inflection of the nonpast imperfective is shown. 6. Note that a few intransitive/unaccusative verbs form their jussive with the template -C 1 C 2 äC 3 - (Meyer 2016a: 173-174). Derived stems do not combine with the set of templates for simplex roots, but with their own set of templates, as shown for the mediopassive of 'bury' in Figure 3. 7 In contrast to the simplex root √ k' 1 -b 2 -r 3 in Figure 2, the mediopassive form only has the sense of 'bury', not of 'plant'. Furthermore, a separate template for the negative perfective is lacking, and the vocalization/gemination of the templates for the imperfective and jussive conjugations differ from the simplex root in Figure 2.

Like simplex stems, derived stems are formed through the interdigitation of stem-specific conjugational templates and the lexical root (stage (i) in Figure 2 and Figure 3). Thus, the derived stems are not simply a modification of the corresponding simplex stem. However, they are morphologically more complex or augmented than the corresponding simplex stems due to the presence of the derivational prefixes, a-insertion or reduplication.

Regarding the function of the productive derivational prefixes, the mediopassive t(ä)-typically reduces the valence of a transitive verb by deleting its agent participant, as in (2c) täʔäbbäräm 'be buried' from the transitive verb k'äbbäräm 'bury' (2a). The direct causative a-increases the valence of an intransitive verb by adding an agent, as in (3b) aʔärräbäm 'bring near' related to the simplex form k'ärräbäm 'be near' (3a). The indirect causative at-can be applied to intransitive and transitive verb roots and introduces the new role of a causer or facilitator of the verbal event, as shown in (2b) atk'äbbäräm 'let bury' and (3c) atkʲärräbäm 'let bring near'. However, in contrast to other Ethiosemitic languages, particularly Amharic, it is not possible to predict which verb roots allow for the mediopassive and the two causative derivations, i.e. derivation is rather a lexical feature connected with the verb root.

7. For the mediopassive of √ k' 1 -b 2 -r 3 'bury', no verb is attested in the jussive/imperative. Therefore, the verbal noun, which is also based on the jussive template, is given in Figure 3.

The noncausal/causal verb correspondences in Muher

This section first gives a statistical overview of the noncausal/causal verb alternation in Muher based on the 31-item wordlist in [START_REF] Haspelmath | More on the typology of inchoative/causative verb alternations[END_REF], and then discusses a number of topics related to them.

List of noncausal/causal verbs in Muher

The Muher equivalents of the 31 noncausal/causal verb pairs in Haspelmath (1993: 97) and their correspondence types are shown in Table 1. Haspelmath (1993: 90-92) distinguishes five strategies for the formal relationship of the two verbs in noncausal/causal pairs, namely causativization (C), in which a causal verb is derived from a noncausal simplex verb; anticausativization (A), which is the reverse, i.e. a noncausal verb is derived from a causal simplex verb; equipollence (E), in which both verbs are derived; suppletion (S) for pairs with two formally unrelated lexical verbs; and lability (L) when a single verb has a causal and a noncausal meaning. The anticausative and causative correspondences are directed in the sense that the morphologically more marked verb is derived from the simplex verb. The remaining three types -equipollence, suppletion, and lability -are nondirected, i.e. a distinction between simplex and derived verb cannot be made. A modification of [START_REF] Haspelmath | More on the typology of inchoative/causative verb alternations[END_REF] original list is found with the verb 'freeze' (#2). Its direct translation into Muher would be a periphrastic expression similar to 'become ice' and 'prepare/make ice'. Therefore, this verb was replaced with the pair 'be cool/cool'. Muher has no dedicated verb 'sink' (#6). This sense is expressed by the polysemous verb bʷännam whose lexical meaning can be generally described as 'disappear from view by penetrating into something, like a field, a hole, water, etc.' Furthermore, some derived noncausal mediopassive verbs in Table 1 have a more specialized semantics than the corresponding causal verbs (#26, 27, 29, 30). These instances have been counted as formal noncausal/causal pairs, even if the noncausal sense is typically expressed by the impersonal (see §5). In addition, the mediopassive verb tänässam appears twice in Table 1: as the suppletive noncausal counterpart to k'ɨsäkk'äsäm 'wake up' (#4), where it can also be replaced by the impersonal of k'ɨsäkk'äsäm, and as the derived noncausal counterpart to the verb nässam 'raise' (#21). The two instances of tänässam have been counted as distinct pairs.

The noncausal verb alläfäm 'improve' (#22) lacks a causal counterpart and will thus not be considered further. Conversely, the causal verbs 'begin' (#16), 'connect' (#24), and 'change' (#25) have no derived noncausal counterpart, and thus are not counted. However, in contrast to alläfäm 'improve', these verbs can form the impersonal with an unspecific subject (see §5). Consequently, the number of pairs in [START_REF] Haspelmath | More on the typology of inchoative/causative verb alternations[END_REF] list is reduced from 31 to 27 noncausal/causal verb pairs. One of these English pairs, #17, has two translational equivalents in Muher: bratʧ'äm in the sense of 'disperse' or abättätäm meaning 'widen'. As suggested by Haspelmath (1993: 100), each correspondence in the double pair #17 has been counted with 0.5 for the overall calculation in Table 2. As can be seen from Table 2, all five strategies for the formation of noncausal/causal verb pairs are found in Muher. The majority of the verb pairs (17 pairs or 63%) are directed, with a preference for the causativization strategy (10.5 pairs), while the anticausativization strategy is less frequent (6.5 verb pairs). A summary of the directed pairs is given in Table 3 which shows that causativization and anticausativization are marked respectively by a single productive derivational strategy, namely the prefixation of tä-for the mediopassive and of a-for the direct causative. The productive indirect causativization by the prefix at-, as well as the nonproductive derivations by the prefixes ɨn-(intr) and an-(tr) are not employed in these verb pairs. The first half of Haspelmath's (1993: 97)

Formation of directed pairs

In directed pairs, the noncausal simplex verbs in Table 3 derive their causal counterpart by the direct causative prefix a-plus the respective inflectional template (see §2). As a result, intransitive simplex verbs change into transitive verbs with an agentive subject, which is usually animate.

(4) a. gɨfʷätwe t'ärräʔäm.

gɨfʷät-we t'ärräk'-ä-m clothes-def dry\pfv-sbj.3sg.m-csm 'The clothes have dried.' b. rawda gɨfʷätwe at'ärräʔättɨnnɨm.

rawda gɨfʷät-we a-t'ärräk'-ätt-nn-m Rawda.f clothes-def caus.dr-dry\pff-sbj.3sg.f-obj.3sg.m-csm 'Rawda dried the clothes.'

In some instances, the direct causative prefix a-also attaches to transitive (causal) simplex verbs which then have a more specialized meaning, see, e.g., (18b) in §4, or they may convey an indirect causal sense, as with the transitive verb läbbäsäm 'dress' in (5), which is in other verbs expressed by prefixing at-(caus.idr).

(5) kädɨr jägʷɔbbehut aläbbäsännɨm.

kädɨr jä-gʷäbbe-hʷta a-läbbäs-ä-nn-m Kadir.m gad-brother-poss.3sg.m caus.dr-dress\pfv-sbj.3sg.m-obj.3sg.m-csm a. 'Kadir dressed his brother.' (direct causative reading) b. 'Kadir provided clothes for his brother.' (indirect causative reading) Simplex verbs with the root-initial vocalic radical a, e.g. addäräm 'spend the night' cannot form their causal counterpart by the direct causative prefix a-, but use the indirect causative prefix at-instead. The resulting derived verbs then have a direct or an indirect causative reading, i.e. atäddäräm (<*at-addär-ä-m caus.idr-spend_the_night\pfv-3sg.m-csm) 'give lodging for the night (direct causative); let pass the night (indirect causative)'.

The causal simplex verbs in Table 3 employ the mediopassive derivation with the prefix tä-(plus the respective mediopassive template) to form their noncausal counterparts. In the mediopassive derivation, unaffected agents are deleted, and the verb valence is reduced by one, as shown with the causal simplex verb in (6a) vis-à-vis its noncausal counterpart in the mediopassive derivation in (6b).

(6) a. kädɨr sinwe säbbärännɨm.

kädɨr sin-we säbbär-ä-nn-m Kadir.m cup-def break\pfv-sbj.3sg.m-obj.3sg.m-csm 'Kadir broke the cup.' b. sinwe täsäbbäräm.

sin-we tä-säbbär-ä-m cup-def mp-break\pfv-sbj.3sg.m-csm 'The cup broke/is broken.'

In (7), the subject kädɨr may have had a role in the instigation of the verbal event, but he is also affected by it and thus treated as a patient. The second noun, äʤhut 'his hand', is not a direct object (as it cannot be cross-referenced by the object suffixes on the verb, even if it is definite), but an adjunct to the verb that specifies the place of the broken body part.

(7) kädɨr äʤhut täsäbbäräm. kädɨr äʤ-hut tä-säbbär-ä-m Kadir.m hand-poss.3sg.m mp-break\pfv-sbj.3sg.m-csm 'Kadir has broken his hand (lit. is broken with respect to his hand).'

Basically, the mediopassive denotes events in which the subject encodes an affected agentive or patientive participant. In contrast to other Ethiosemitic languages outside Gunnän Gurage, the Muher mediopassive has often developed a more specialized, less predictable meaning vis-à-vis the related simplex verb (see §4). Moreover, a noncausal sense is typically expressed by the impersonal construction in Muher (for which see §5).

Correspondences in nondirected pairs

Lability

Labile verbs are rare in Muher. The only instance found so far is the verb männam 'fill' (#14 in Table 1), which occurs in the same form in noncausal and causal contexts (8).

(8) a. bʷɔtto bɨga männam.

bʷätto bä-ɨga männa-ä-m river mil-water fill\pfv-sbj.3sg.m-csm 'The river is full of water.' b. räwda sinwe bäʔawa männattum.

räwda sin-we bä-k'awa männa-ätt-u-m Rawda.f cup-def mil-coffee fill\pfv-sbj.3sg.f-obj.3sg.m-csm 'Rawda filled the cup with coffee.'

The major difference between (8a) and (8b) is the number of involved arguments: the noncausal intransitive verb männam in (8a) only has a subject, while the causal transitive verb männam has an additional object. Besides, the two subjects differ in animacy. The noncausal use coincides with inanimate patient-like subjects, while in causal events the subject is an agent. Note that in (8b) the use of the object suffix on the verb is obligatory because its referent, the primary object sinwe 'the cup', is definite (see §2).

Equipollence

Formally, all pairs in Table 1 could be analyzed as equipollent because both the simplex verb and its derived counterpart are formed through stem-specific templates which interdigitate with a lexical root (similar to the conjugation class change in the typology of [START_REF] Nichols | Transitivizing and detransitivizing languages[END_REF], and not simply by attaching derivational prefixes to a simplex stem (see §2). Nevertheless, simplex verbs can clearly be distinguished from their derived counterparts through the lack of such an additional derivational prefix. In the equipollent pairs discussed here, however, the two verbs of the pair are equally marked by a derivational prefix, i.e. the two verbs are equally morphologically augmented.

Equipollent correspondences can be classified into three types, of which only two are attested in the dataset in Table 1. The first type (#18 and #23 in Table 1) is characterized by nonproductive derivational prefixes, namely ɨn-for intransitive verbs (noncausal) and an-for transitive verbs (causal), as with the verb 'roll' in (9).

(9) a. ɨʃɨttawe wɔhättawe garɨnnɨjä ankɨballälmem.

ɨʃɨtta-we wähätta-we gar-ɲɲɨjä an-kballäl-ma-i-m woman.pl-def ensete_tuber.def house-dir tr-roll\pfv-sbj.3pl.f-obj.3sg.m-csm 'The women rolled the ensete tuber towards the house.' b. ɨnnamwe bägʷɔʤä ɨnkɨballäläm.

ɨnnam-we bä-gʷäʤä ɨn-kballäl-ä-m cow-def mil-hole intr-roll\pfv-sbj.3sg.m-csm 16 'The cow rolled into a pit.'

In the second type (#7 and #19 in Table 1), the two verbs of a pair are formed by the productive mediopassive and indirect causative derivations, marked by the prefixes tä-and at-, respectively, as with the verb 'develop' in (10).

(10) a. rawda gunnänça atläʔättum. rawda gunnän-hʲa at-läk'-ätt-u-m Rawda.f hair-poss.3sg.f caus.idr-develop\pfv-sbj.3sg.f-obj.3sg.m-csm 'Rawda let her hair grow.' b. kunʧɨfdähä lɨkk'e tälaʔäm.

kunʧif-dähä lɨkk'e tä-lak'-ä-m beard-poss.2sg.m very mp-develop\pfv-sbj.3sg.m-csm 'Your beard grew a lot.' For three of the four equipollent verb pairs, i.e. 'learn/teach' (#7), 'roll' (#18), and 'rock' (#23), 17 an underived simplex stem is unattested. The fourth verb pair, 'develop' (#19), has the simplex laʔäm 'surpass' (11a), but its semantic relation to the derived stems, i.e. the indirect causative (11b) and the mediopassive (11c), is not straightforward.

In the indirect causative derivation, type A simplex verbs consistently change to type B verbs (for the verb types, see §2). Therefore, the vocalic radical a of the simplex verb laʔäm (11a) 18 gets palatalized and changes to ä, and the three basic conjugations use a single template realized as läʔ (recall that postvocalic k' is uttered ʔ), as in (11b).

(11) a. Simplex laʔ-ä-m jɨ-lk'-u jä-laʔ surpass\pfv-sbj.3sg.m-csm sbj.3sg.m-surpass\ipfv-csm sbj.3sg.m-surpass\juss 'he surpassed' 'he surpasses' 'let him surpass'

16. Nouns with non-human referents are uniformly marked with the 3sg.m subject marker in the singular, even if they are semantically feminine like ɨnnam 'cow'. 17. This verb exists in another stem derived by a-insertion, namely k'ɨjakk'äʲäm 'mix'. 18. The verb laʔäm 'surpass' originates from the type A triliteral root √ l 1 -A 2 -k' 3 , with a glottal stop, a velar, a pharyngeal or a glottal fricative (generally referred to as gutturals) as second consonant, which changed to a vocalic radical (represented by the symbol A) that is mainly uttered as vowel a attached to the initial root consonant. Historically, the gutturals triggered vowel assimilation processes that still can be observed in the irregular imperfective base of these roots with type A simplex verbs, in which the vocalic radical is lacking, as in jɨlk'u 'he surpasses' in (16a).

b. Indirect causative atat-läʔ-ä-m j-at-läʔ-u caus.idr-develop\pfv-sbj.3sg.m-csm sbj.3sg.m-caus.idr-develop\ipfv-csm 'he developed' 'he develops' The nonproductive derivational prefixes ɨn-(intr) and an-(tr) are limited to quadriliteral verbs which express some kind of movement, 19 while the mediopassive and indirect causative derivations with the prefixes tä-and at-occur with all other verbs. The third type of equipollent correspondence, not attested in the dataset in Table 1, are complex predicates (also known as compound or composite verbs) 20 with the inflected verbs bäʲäm 'say' and amäɲɲäm 'make' 21 as light verb and an invariable ideophone as converb, as in ( 12). Complex predicates with the light verb 'say' express noncausal events, whereas predicates with 'make' express causal events.

(12) a. kaʃʃɨ bäʲäm (noncausal) vs. kaʃʃɨ amäɲɲäm (causal) 'be/make very dry' b. ʧ'ɨfɨff bäʲäm (noncausal) vs. ʧ'ɨfɨff amäɲɲäm (causal) 'be/make completely full' c. t'ämbʷa bäʲäm (noncausal) vs. t'ämbʷa amäɲɲäm (causal) 'crack/let crack (with a sharp noise)'

The complex predicates in (12a-b) are semantically related to the lexical verbs 'dry' and 'fill' (#3 and #14 in Table 1), but have an additional intensive meaning component. Interestingly, the verb amäɲɲäm 'make' is only attested as light verb in complex predicates, as in (#12), whereas elsewhere the verb k'ʲännam 'make' is used, e.g. (#13).

(13) tähank'ʲäta k'ämäle jägɨzz wɔdʤä jʊwä ͡ εt aʃkɨr kʲ'ännɨmɥim tähank'ʲäta k'ämäle jä-gɨzz wädʤä j-wäʲ-ät then monkey gad-cattle herd 3sg.m-spend_the_day\ipfv-purp aʃkɨr k'ʲänna-mʷ-i-m laborer make\pfv-sbj.3pl.m-obj.3sg-m-csm 'Then they made the monkey a shepherd to look after [lit. to spend the day with] the herd of cows.' 19. For the related derivational prefix ɨn-and an-in Amharic, see Baye (1999: 58-59). 20. For an overview of complex predicates in Ethiosemitic, see [START_REF] Meyer | The quotative verb in Ethiosemitic languages and in Oromo[END_REF][START_REF] Amberber | The structure of the light verb construction in Amharic[END_REF]. 21. This verb is possibly a loan from Ezha or Chaha amän(n)äm 'make' (Leslau 1979b: 406).

Suppletion

The dataset in Table 1 contains five suppletive pairs, namely 'wake up' (#4), 'dissolve' (#11), 'burn' (#12), 'finish' (#15), and 'die/kill' (#31). In these pairs, the noncausal verbs and their causal counterparts are not formally related, as illustrated in ( 14). ( 14) a. assɔwä eʔäm.

assäwä ek'-ä-m salt be_finished\pfv-sbj.3sg.m-csm 'The salt is finished.' b. ʃärätwe guggurɨm fädʤännɨm.

ʃärät-we guggurɨm fädʤä-ä-nn-m food-def all finished\pfv-sbj.3sg.m-obj.3sg.m-csm 'He finished all the food.'

In all four pairs, the noncausal counterpart can be replaced by the impersonal form of the causal verb, see, e.g., (25) in §5.

Additional derived verbs for the pairs in Table 1

Some of the transitive simplex verbs, particularly in the last third of Table 1, also derive another causal verb, as shown in Table 4. Thus, the indirect causative increases the valence by one, so that a transitive verb becomes ditransitive by adding a causer subject, while the actual agent, which fulfils the verbal action, is usually not overtly expressed. However, if it is, it is transferred to the peripheral object position marked by the instrumental prefix bä-(15c), or it becomes a primary object, as in (16b).

In contrast to other Ethiosemitic languages, such as Amharic (see Meyer 2011b: 1198) or Wolane (Meyer 2006: 74-78), the occurrence of the indirect causative in Muher is less productive and less predictable, as it can only be formed from a limited number of verbs. 24 Moreover, the meaning of the indirect causative derivation is sometimes included as an optional reading of the direct causative, as in (5) above, or with a-ʔärräsäm 'make start; let start' (#16 in Table 4). Consequently, the indirect causative verb in (16b) could also be replaced with the direct causative (17) without any observable change in meaning.

(17) sitti jäjunus k'uran wɔʔɨra aʔ'ärräsättum. sitti jä-junus k'uran wä-k'ra a-k'ärräs-ätt-u-m Sitti.f gad-Yunus.m Quran vn-read\juss caus.dr-begin\pfv-sbj.3sg.f-obj.3sg.m-csm 'Sitti incited Yunus to start reading the Quran.' 23. The second-and third-person plural markers express politeness with singular referents. 24. The reason for the limited occurrence of the indirect causative is not yet clear. It might be a more general feature of Gunnän Gurage, or due to some kind of grammatical restructuring in the Muher grammar of my consultants who only speak it as a heritage language.

In other instances, the direct causative has acquired a more specialized meaning than its simplex form, as is the case with direct causative a-ʃäggäʲäm -simplex ʃäggäʲäm 'change' (#25 in Table 4)which has the unpredictable meaning 'exchange (item X for item Y)', as in ( 18):

(18) a. junus jäβollätʧähut gʷɔtʧä ʃäggä ͡ ɛnnɨm. junus jä-bʷällätʧä-hʷt gʷätʧä ʃäggäʲ-ä-nn-m Yunus.m gad-wedding-poss.3sg.m date change\pfv-sbj.3sg.m-obj.3sg.m-csm 'Yunus changed the date of his wedding.' b. sämir ɨnnamhut bäβɨʔuwä aʃäggä ͡ ɛnnɨm.

sämir ɨnnam-hʷt bä-bɨʔuwä a-ʃäggäʲ-ä-nn-m Samir.m cow-poss.3sg.m mil-mule caus.dr-change\pfv-sbj.3sg.m-obj.3sg.m-csm 'Samir exchanged his cow for a mule.'

Table 5 shows two derived mediopassive verbs which occur in addition to the basic noncausal/causal verb pair. The additional mediopassive verbs denote a slightly modified verbal situation compared to the simplex form from which they are derived. Thus, tä-bt'äbbät'äm, the mediopassive of bɨt'äbbät'äm 'dissolve' (#11 in Table 5), does not simply express the passive reading 'be dissolved', as the basic noncausal verb t'ämmam in (19b), but conveys in addition the sense of being not properly dissolved so that the liquid is now lumpy, clotted or turbid (19c-d) ). However, not all Muher consultants agreed about the more specialized reading of these mediopassive verbs, rather considering them to be a noncausal construction without a known instigator of the verbal event.

The impersonal construction

Verbal derivation (see §3.2) formally marks the grammatical relationship in noncausal/causal verb pairs by changing the valence and voice of one of its members. In addition, Muher frequently makes use of a morphosyntactic construction, the impersonal, which does not affect the argument structure of a verb but which systematically bleaches out the subject and consequently foregrounds the object and its patient role. The impersonal passive is almost regularly formed from any verb with a potential human subject (for an overview, see Biruk 2013). It occurs with simplex verbs, e.g. arräʧ'äm 'cut' and färrähäm 'be patient' in (20), as well as with derived verbs, like the direct causative abässam 'bring (i.e. make come)' in ( 21) and the mediopassive täkäbbäräm 'be honored' in ( 22).

(20) fʷämfʷä arräʧ'ɨm ʃɨja jʊwajtt, färrähum bäsäβ jɨsälett. fʷämfʷä arrät'-ʷʲ-m ʃɨjä j-bäʲa-ʷʲ-i-tt ensete_sprout cut\pfv-ips-cvb flower ips-eat\ipfv-ips-obj.3sg.m-csm färräh-ʷʲ-m bä-säb j-säla-ʷʲ-i-tt be_patient\pfv-ips-cvb mil-human ips-arrive\ipfv-ips-obj.3sg.m-dcm 'After one has cut the ensete sprout [which is growing in bundles from the root of an old plant], one eats (its) flower [i.e. the ripe ensete plant], and after one has been patient, one reaches [being a respected] human.' (proverb) (21) k'awa bägʷarra läkk'ämum jaβäʃett.

k'awa bä-gʷarra läkk'äm-ʷʲ-m j-a-bäsa-ʷʲ-i-tt coffee mil-garden pick\pfv-ips-cvb ips-caus.dr-come\ipfv-ips-obj.3sg.m-csm 'One brings the coffee (beans) after having picked them in the garden.' The impersonal is not a separate conjugation, but an inflectional category, a kind of fourth person, with a dedicated subject marker on the verb, namely the suffixed nonsegmental vocoid ʷʲ which triggers the labialization of labial, velar and glottal root consonants in a verb base, and the palatalization of alveolar, velar and glottal consonants base-finally (see Rose 2000: 110-111;Meyer 2019: 232). The impersonal subject suffix attaches to regular conjugational verb bases, but not to the verbal noun. In the perfective, the impersonal subject suffix is in complementary distribution with the personal subject affixes ( 22), but in the imperfective and jussive conjugations, the impersonal suffix co-occurs with the regular third person prefix j(ä)-, as on the clause-final verbs in ( 20) and ( 21). Impersonal verbs only marked by the vocoid (and the third person subject prefixes) are limited to same-subject converbs, as in arräʧ 'ɨm 'one having cut' in (20) or läkk 'ämum 'one having picked' in (21). Usually, the impersonal subject marker co-occurs with an object suffix, by default the 3sg.m, as on the transitive verbs jʊwajtt 'one eats ' and jaβäʃett 'one brings' in (20) and ( 21), as well as on the intransitive verbs jɨsälett 'one arrives at/reaches' and täkäbbʷɔrim 'one is honored' in ( 20) and ( 22). With transitive verbs, the default 3sg.m object suffix may be replaced by another suffix referring to the actual person in the patient role, e.g. the 3sg.f in ( 23). ( 23) jägärädwe ɔ ͡ atʧ'äm bɔ ͡ anɟɨβat t'äkk'ojam.

jä-gäräd-we a-wätt'a-ʷʲ-m bä-wanɟɨbat gad-girl-def caus.dr-take_out\pfv-ips-cvb mil-upper_door_beam t'äkk'äʲ-ʲʷ-ja-m hide\pfv-ips-obj.3sg.f-csm 'The girl was taken out (lit. one took the girl out) and hidden (lit. one hid her) on lintel.'

The impersonal semantically backgrounds the agentive subject with an obligatory human referent, which is unspecified and irrelevant to the verbal event, while the patientive object is foregrounded. With intransitive verbs, such an unspecified subject has the generic reading 'people'.

The impersonal encodes the same function as the mediopassive and noncausal verbs with a human subject, and as such this construction is frequently used instead of the latter two. For the verb pairs in Table 1, the impersonal forms shown in Table 6 are attested. The impersonal verbs in brackets can only be used with assumed human referents and have a metaphorical sense. #30), usually employ the impersonal of the transitive verb rather than their derived mediopassive counterpart, to express events with a foregrounded patientive object. Generally, a noncausal verb differs from the verb in the impersonal with regard to the conceptualization of the situation. The impersonal typically conveys a reading with a foregrounded patient that is affected by an implied but unspecified human instigator (24b, d), while the noncausal verb lacks such an opaque instigator (24c, d).

(24) a. rawda tɨkäça att'äβättum. rawda tɨkä-hʲa att'äb-ätt-u-m Rawda.f child-poss.3sg.f wash\pfv-sbj.3sg.f-obj.3sg.m-csm 'Rawda washed her child.' b. jä-rawda tɨkä att'äwim.

jä-rawda tɨkä attäb-ʷʲ-i-m gad-Rawda.f child wash\pfv-ips-obj.3sg.m-csm 'Rawda's child is washed (i.e. one washed Rawda's child).' c. jä-rawda tɨkä tatt'äβäm.

jä-rawda tɨkä t(ä)-att'äb-ä-m gad-Rawda.f child mp-wash\pfv-sbj.3sg.f-csm 'Rawda's child is washed/washed himself.' d. sin att'äwim. / *tatt'äβäm. sin attäb-ʷʲ-i-m t(ä)-att'äb-ä-m cup wash\pfv-ips-obj.3sg.m-csm mp-wash\pfv-sbj.3sg.f-csm '(The) cups have been washed.' With the impersonal in (24b), it is evident that the child did not wash himself but that someone washed him. In (24c), in contrast, the mediopassive verb is ambiguous. It is usually implied that the child washed himself (reflexive reading), but also another person may have washed him (passive reading). With a non-human entity as patientive object, as in (24d), only the impersonal can be used to foreground it (passive-like reading), whereas the mediopassive would yield an unacceptable expression (at least for some speakers). 25 The same phenomenon can also be observed with the suppletive pairs 'wake up' (#4) 'dissolve' (#11), 'burn' (#12), 'finish' (#15), and 'die/kill' (#31). When the reading of the noncausal verb, e.g. eʔäm 'be finished' (14a) or mʷätäm 'die' (26), is contrasted with the impersonal of its causal counterpart, i.e. fʷɔdʤim 'one finished' (25) from fädʤäm 'finish ' and k'ätt'o ͡ jm 'one killed' from kätt'äʲäm 'kill' (26), then the impersonal clearly supposes that there is a person who finished the food or the salt, but the identity of this person is not relevant to the discourse. With the noncausal verb, such an interpretation is lacking.

(25) ʃärätwe/assɔwäwe fʷɔdʤim.

ʃärät-we assäwä-we fädʤä-ʷʲ-i-m food-def salt-def finish\pfv-sbj.3sg.m-obj.3sg.m-csm 'The food/salt is finished (lit. one finished the food/salt).' 25. Some Muher speakers accept the mediopassive of 'wash' with nonhuman nouns, when there is no information about the instigator of the event, i.e. whether the cups have been washed by a person or, for instance, by the rain.

(26) mʷɔtäm vs. k'ätt'ä ͡ ɛm vs. k'ätt'o ͡ jm mʷät-ä-m k'ätt'äʲ-ä-m k'ätt'äʲ-ʷʲ-i-m die\pfv-sbj.3sg.m-csm kill\pfv-sbj.3sg.m-csm kill\pfv-ips-obj.3sg.m-csm 'he died' 'he killed' 'he was killed; one killed him'

Thus, the difference between a noncausal verb and the impersonal has mainly to do with a possible or hypothetical instigator of the expressed verbal event. Although the two constructions differ formally, functionally they both express a verbal event with a foregrounded patientive object. Moreover, the impersonal is preferred in this type of construction and seems to be in the process of replacing the mediopassive derivation, which in turn is often no more available as a verbal derivation of a causal verb or has acquired a more specialized meaning.

Conclusion

Out of the 31 noncausal/causal pairs in [START_REF] Haspelmath | More on the typology of inchoative/causative verb alternations[END_REF] list, only 27 occur in Muher. Four verb pairs from Haspelmath lack a noncausal verb in Muher, and for one verb pair the causal member is unattested in Muher.

For the formation of noncausal/causal pairs, the directed alternation prevails in Muher. It amounts to about two-third of the data. Furthermore, the causative strategy is more frequent than the anticausative strategy. The latter seems to be in the process of being replaced by a functionally similar morphosyntactic verb form, the impersonal. The impersonal is currently the most productive way to express verbal events with a foregrounded patientive constituent, while the mediopassive has acquired more specialized semantics in many cases. In directed pairs, the derived causal verb is usually marked by the direct causative prefix a-, and the derived noncausal verb by the mediopassive prefix tä-.

Within the smaller set of nondirected alternations, only one labile verb is attested in the dataset: männam 'fill', whereas suppletive and equipollent pairs are almost equally frequent. The two verbs in the equipollent pairs contain derivational prefixes, either mediopassive tä-vs. direct and indirect causative a-and at-, or the nonproductive intransitive and transitive derivations marked by the prefixes ɨn-and an-. In addition, equipollent pairs marked by light verbs (bäʲäm 'say' for the noncausal and amäɲɲäm 'make' for the causal correspondent) express the causal alternation with complex predicates, which are not included in the Muher translational equivalents of the original 31-item list.

In addition to the basic noncausal/causal alternation, a number of verbs have secondary correspondents, which for causal expressions are typically derived by the indirect causative marked by at-, while a secondary noncausal correspondent usually occurs in the mediopassive derivation.

Figure 2 -

 2 Figure 2 -Basic inflection of the root √ k' 1 -b 2 -r 3 'plant; bury' (type A) in the simplex stem

Figure 3 -

 3 Figure 3 -Basic inflection of the root √ k' 1 -b 2 -r 3 'bury' in the mediopassive stem

(

  15) a. wɔndɨmu sällämäm. wändɨmu sälläm-ä-m Wendimu.m be(come)_Muslim\pfv-sbj.3sg.m-csm 'Wendimu became Muslim.' b. aʃɨrgeta jäwɔndɨmu asällämmɥ ͡ im. aʃɨrgeta jä-wändɨmu Quran_teacher gad-Wendimu.m a-sälläm-mʷ-i-m 23 caus.dr-be(come)_Muslim\pfv-sbj.3pl.m-obj.3sg.m-csm 'The Quran teacher converted Wendimu to Islam (by making him say the shahada).' c. jäwɔndɨmu -junus baʃɨrgeta assällämännɨm. jä-wändɨmu junus bä-aʃɨrgeta gad-Wendimu.m Yunus.m mil-Quran_teacher at-sälläm-ä-nn-m caus.idr-be(come)_Muslim\pfv-sbj.3sg.m-obj.3sg.m-csm 'As for Wendimu, Yunus incited him to convert to Islam through the Quran teacher.' (16) a. junus k'uran wɔʔɨra k'ärräsäm. junus k'uran wä-k'ra k'ärräs-ä-m Yunus.m Quran vn-read\juss begin\pfv-sbj.3sg.m-csm 'Yunus began to read the Quran.' b. sitti jäjunus k'uran wɔʔɨra at'k'ärräsättum. Quran vn-read\juss at-k'ärräs-ätt-u-m caus.idr-begin\pfv-sbj.3sg.f-obj.3sg.m-csm 'Sitti incited Yunus to start reading the Quran.'

( 22 )

 22 ahuɲɲaʃ bäʧ'äɲɲi sällem täkäbbʷɔrim. ahuɲɲa-ʃ bä-ʧ'äɲɲä-ʷʲ-i sälla-ʷʲ-i-m now-foc mil-beget\pfv-ips-obj.3sg.m[rel] arrive\pfv-ips-obj.3sg.m-cvb tä-käbbär-ʷʲ-i-m mp-honor\pfv-ips-obj.3sg.m-csm 'But now, one is honored because [the generation] which one begat has arrived.' [i.e. aged parents start to benefit from their children after they have grown up and can protect and support them]

Table 1 -

 1 Noncausal/causal pairs in Muher

	1	boil 8		fännam	afännam	C	caus.dr
	2	*freeze > be cool/cool gɨzäggäzäm	agzäggäzäm	C	caus.dr
	3	dry		t'ärräʔäm	at'ärräʔäm	C	caus.dr
	4	wake up		tänässam 9	k'ɨsäkk'äsäm S
	5	go out		wätt'am	ɔ ͡ att'am 10	C	caus.dr
	6	sink		bʷännam	abʷännam	C	caus.dr
	7	learn/teach		tämaräm	atmäräm 11	E	mp/caus.idr
	8	melt		k'ällät'äm	aʔällät'äm	C	caus.dr
	9	stop		k'ʷämäm	aʔʷämäm	C	caus.dr
	10	turn (around)	ʒɔ ͡ aräm	aʒɔ ͡ aräm	C	caus.dr
	11	dissolve		t'ämmam	bɨt'äbbät'äm S
	12	burn		näddädäm	mäɟɟäm	S
	13	destroy		färräsäm	afärräsäm	C	caus.dr
	14	fill		männam	männam	L
	15	finish		eʔäm	fädʤäm	S
	16	begin		-	k'ärräsäm	-
	17	spread >	disperse widen	a. täbratʧ'äm bratʧ'äm b. bättätäm abättätäm	A C	mp caus.dr
	18	roll		ɨnkɨballäläm	ankɨballäläm E	intr/tr

8. Although this verb pair is attested (see, for instance, Leslau 1979a: 877), fännam 'boil (intr)' tends to be replaced by the verb ɨnsɨrässäräm 'boil with a bubbling sound' and afännam 'boil (tr)' by ʧäkkäʲäm 'boil, cook in hot water'. My Muher consultants often avoid fännam/afännam 'boil' due to the similarity with the verb fʷännam 'have sexual intercourse'. A similar phenomenon can be observed with the Muher ditransitive verb bäddam 'take something from someone', which tends to be replaced by the Amharic loan verb wässädäm 'take' because Muher bäddam closely resembles Amharic bädda 'have sexual intercourse' (see also

Leslau 1959: 107)

. 9. Mediopassive with a specialized semantic vis-à-vis the causal verb: 'rise' (#21). 10. The verb ɔ ͡ att'am 'go out; descend', see also

(#24)

, represents underlying *a-wätt'a-m caus.dr-go_out\pfv.3sg.m-csm, in which the sequence /awä/ is pronounced as a diphthong [ɔ ͡ a]

(Meyer 2019: 231; Leslau 1979b: xvii)

. 11. Another causal form is astämmäräm, most probably a loan from Amharic astämarä.

Table 2 -

 2 Distribution of noncausal/causal correspondences in 23 Muher pairs

		Directed pairs	Nondirected pairs		Total
		Causative Anticausative Suppletive Equipollent Labile
	Raw	10.5	6.5	5	4	1	27
	Percent	39%	24%	19%	15%	3%
	Total percent		63%			37% 100%

12. Mediopassive with a specialized semantic vis-à-vis the causal verb: 'gather (only human)'. 13. Mediopassive with a specialized semantic vis-à-vis the causal verb: 'hardly open'. 14. Mediopassive with a specialized semantic vis-à-vis the causal verb: 'hardly close. 15. Mediopassive with a specialized semantic vis-à-vis the causal verb: 'split easily'.

Table 3 -

 3 Anticausative and causative strategies

	#	Causal simplex	Noncausal	# Noncausal simplex	Causal
		(6.5 pairs)	derivation	(10.5 pairs)	derivation
	17 spread(> disperse) mp	1 boil	caus.dr
	21 rise/raise	mp	2 be cool/cool	caus.dr
	26 gather*	mp	3 dry	caus.dr
	27 open*	mp	5 go out/put out	caus.dr
	29 close*	mp	6 sink	caus.dr
	30 split*	mp	8 melt	caus.dr
	28 break	mp	9 stop	caus.dr
				10 turn	caus.dr
				13 destroy	caus.dr
				17 spread (> be wide) caus.dr
				20 get lost/lose	caus.dr

*More specialized semantics vis-à-vis the causal simplex verb

  The more frequent type -noncausal with the verbs in the first part of Table1, but causal with the verbs towards the end -is then expressed by the simplex stem, while the respective other verb is morphologically marked by derivation. The two Muher equivalents for English 'spread' (#17) seem to reflect this semantic division. If 'spread' is translated into Muher in the sense of 'disperse', the unmarked simplex verb is causal with a human agent as subject. The other member of the pair is then a derived noncausal verb in the mediopassive. By contrast, if 'spread' is translated in the sense of 'be wide/ widen', the unmarked simplex verb is typically noncausal, without a human instigator. Consequently, its counterpart is a derived causative verb.With regard to the non-directed pairs, the only labile verb is 'fill' (#14) (see §3.3.1), nine other pairs display either equipollent or suppletive alternations (see § §3.3.2 and 3.3.3).

noncausal/causal verb pairs in Table

1

is dominated by the causativization strategy, whereas the derived noncausal verbs are found in the second half.

[START_REF] Haspelmath | Coding causal-noncausal verb alternations: A form-frequency correspondence explanation[END_REF]

attributes this phenomenon to the lexical semantics of the verbs. While events in the first part of Table

1

like 'dry', 'sink', 'melt', etc., usually occur without an agent, the verbs in the second part of the table usually require a human agent who fulfills the verbal action.

Table 4 -

 4 Additional derived causal verbs to the transitive simplex verb in Table1

	# Gloss	Causal simplex Derived causal correspondent Derivation
	25 change ʃäggäʲäm	a-ʃäggäʲäm	caus.dr
	16 begin	k'ärräsäm	at'-k'ärräsäm / a-ʔärräsäm	caus.dr/idr 22
	15 finish	fädʤäm	at-fädʤäm	caus.idr
	24 connect gätt'ämäm	ad-gätt'ämäm	caus.idr
	26 gather sɨbässäbäm	as-sɨbässäbäm	caus.idr
	27 open	käffätäm	at-käffätäm	caus.idr
	28 break	säbbäräm	as-säbbäräm	caus.idr
	30 split	fäkk'am	at-fäkk'am	caus.idr
	31 die/kill k'ätt'äʲäm	at'-k'ätt'äʲäm	caus.idr
	Apart from 'change' (#25) and also 'begin' (#16), which occur in the direct causative derivation
	marked by the prefix a-(plus specific templates), all other additional causal verbs are in the indirect
	causative marked by the prefix at-(and its allomorphs) plus specific templates (see §2). The indirect
	causative derivation adds a new causer as subject, which instigates or facilitates the verbal action, as
	in (15c) and (16b). In contrast, the direct causative derivation increases the valence of an intransitive
	verb by adding a new agentive subject, which is actively involved in the verbal action, and transfers

the subject of the simplex verb to the object position, as in (15a-b).

22. The indirect causative at'-k'ärräsäm 'let begin' and the direct causative a-ʔärräsäm are free variants. Their occurrence depends on currently unknown factors; even the same speaker may use them interchangeably.

Table 5 -

 5 Additional derived noncausal counterparts

	# Gloss	Basic pair Noncausal Causal	Additional noncausal verb	Derivation
	1 boil	fännam	afännam	täfännam	> 'boil over' mp
	11 dissolve t'ämmam bɨt'äbbät'äm täbt'äbbät'äm > 'be turbid' mp

  .Note that a specialized meaning is also found with derived mediopassive verbs as one component of the basic noncausal/causal pairs #26, #27, #29, and #30 in Table1. While the mediopassive of 'gather' (#26), täsbässäbäm, is limited to human subjects, the mediopassive of the remaining three verbs has an additional manner component in which the verbal event occurs, i.e. täkäffätäm 'hardly open' (#27), tatʧ'äm 'hardly close' (#29), and täfäkk'am 'split easily' (#30

	d. bʷɔtto täbt'äbbät'äm.
	bʷätto tä-bt'äbbät'-ä-m
	river mp-dissolve\pfv-sbj.3sg.m-csm
	'The river is turbid.'
	(19) a. basso bɨt'äbbät'xum sätʧähum.
	bässo		bɨt'äbbät'-hʷ-m	sätʧä-hʷ-m
	roasted_barley_flour dissolve\pfv-sbj.1sg-cvb drink\pfv-sbj.1sg-csm
	'I dissolved roasted barley flour (in water) and drank it.'
	b. assɔwä ɨga t'ämmam.
	assäwä ɨga	t'ämma-ä-m
	salt	water be_dissolved\pfv-sbj.3sg.m-csm
	'The salt is dissolved in water.'
	c. k'awawä täbt'äbbät'äm.
	k'awa-wä tä-bt'äbbät'-ä-m
	coffee-def mp-dissolve\pfv-sbj.3sg.m-csm
	'The coffee (powder) is (still) mixed (with the coffee drink) [i.e. the ground coffee has
	not yet settled].'

Table 6 -

 6 Additional impersonal forms to basic verb pairs in Table1alternative to the noncausal derivation. Similarly, the verbs whose noncausal mediopassive derivation has acquired a rather specialized meaning, i.e. 'gather'(#26), 'open' (#27), 'close' (#29), and 'split' (

	#	Meaning	Noncausal	Causal	Impersonal
	1	boil	fännam	afännam	[unclear]
	2	be cool/cool gɨzäggäzäm	agzäggäzäm	-
	3	dry	t'ärräʔäm	at'ärräʔäm	-
					at'ärräʔʷim
	4	wake up	tänässam	k'ɨsäkk'äsäm tänäʃʃem
					k'ʷʊʃäkk'ʷäʃim
	5	go out	wätt'am	ɔ ͡ att'am	wätʧ'em ɔ ͡ atʧ'em
	6	sink	bʷännam	abʷännam	(bʷännem 'people disappeared')
					awännem
	7	learn/teach	tämaräm	atmäräm	tamʷarim
					atmʷärim
	8	melt	k'ällät'äm	aʔällät'äm	[unclear]
	9	stop	k'ʷämäm	aʔʷämäm	k'ʷämʷim
					aʔʷämʷim

Regarding the verb pairs for 'boil' (#1) and 'melt' (#8) no impersonal could be elicited because my consultants do not use them in their speech (for 'boil' see also fn. 8). Table 6 also shows that the three verbs without a noncausal counterpart, namely 'begin' (#16), 'connect' (#24), and 'change' (#25), employ the impersonal to describe events with a foregrounded patientive object, i.e. functionally they represent a morphosyntactic

Acknowledgments

I am very grateful to all my Muher consultants over the years, especially to Abubakr Sherifo and his entire family, and also to Sitti Gragn. I also acknowledge the support from the SFB 295 (Mainz University) and the Norhed project Linguistic Capacity Building.


David Hason
email: david.hasonrudd@student

Huan Huo

Guandong Xu
email: guandong.xu@uts.edu.au

Predicting Financial Literacy via Semi-supervised Learning

Keywords: Financial literacy, semi-supervised regression, unbalanced datasets, unlabelled Data

Financial literacy (FL) represents a person's ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted by exploiting unlabelled recorded data in financial networks via semi-supervised learning (SSL). Measuring and predicting FL has not been widely studied, resulting in limited understanding of customer financial engagement consequences. Previous studies have shown that low FL increases the risk of social harm. Therefore, it is important to accurately estimate FL to allocate specific intervention programs to less financially literate groups. This will not only increase company profitability, but will also reduce government spending. Some studies considered predicting FL in classification tasks, whereas others developed FL definitions and impacts. The current paper investigated mechanisms to learn customer FL level from their financial data using sampling by synthetic minority over-sampling techniques for regression with Gaussian noise (SMOGN). We propose the SMOGN-COREG model for semi-supervised regression, applying SMOGN to deal with unbalanced datasets and a nonparametric multilearner co-regression (COREG) algorithm for labeling. We compared the SMOGN-COREG model with six well-known regressors on five datasets to evaluate the proposed models effectiveness on unbalanced and unlabelled financial data. Experimental results confirmed that the proposed method outperformed the comparator models for unbalanced and unlabelled financial data. Therefore, SMOGN-COREG is a step towards using unlabelled data to estimate FL level.

Introduction

Financial literacy (FL) is an essential skill in the modern world, and is mandatory for consumers operating in an increasingly complex economic society [START_REF] Worthington | Predicting financial literacy in australia[END_REF]. Current economic conditions have raised significant concerns regarding Australians financial security [START_REF] Worthington | Predicting financial literacy in australia[END_REF], particularly for those who lack the resources and skills to withstand downswings in the economy and take advantage of upswings. Several studies have determined there is a need not only for better understanding, but also to improve FL level. Individuals are generally responsible for various financial decisions, most importantly regarding retirement preparation and house financing. Previously studies [START_REF] Worthington | Predicting financial literacy in australia[END_REF] confirmed the relationship between the complexity of these choices and increased stakes, and also highlights consequences from making financial decisions without sufficient FL. Therefore, effective financial management is a critical factor for any organization to achieve efficiency and success in the market [START_REF] Worthington | Predicting financial literacy in australia[END_REF]. Lusardi [START_REF] Lusardi | Financial literacy among the young[END_REF] surveyed FL definition and effects, evaluating FL levels by asking volunteers four questions about compound interest, inflation, time value of money, and risk diversification. He showed that risk diversification was the most challenging question, with only 9% of Australians giving the correct answer.

The current paper assessed the proposed models effectiveness on unbalanced financial network data to predict customer FL in a superannuation company. Measuring FL levels for millions of customers through a particular online survey in each financial period would be extremely time-consuming and expensive, hence SSL, which exploits a small portion of labelled data and a large amount of unlabelled data, is a smart approach. The dataset was built from customers' financial interactions data and labelled according to an online questionnaire similar to Lusardi's study [START_REF] Lusardi | Financial literacy among the young[END_REF]. To our best knowledge, no previous study exploits a large amount of unlabelled data to predict FL level. We used a baseline algorithm in the self-training method with ensemble cross-validation to justify the baseline models robustness on unbalanced dataset(s). We applied SMOGN to oversample values to enable predicting rare or uncommon data in the skewed dataset. Empirical results confirmed that the proposed SMOGN-COREG model outperformed all current models. Thus, including unlabelled examples via SSR methods improves prediction accuracy more than using only labelled data in supervised methods.

The remainder of this paper is organised as follows: Section 2 reviews recent related FL studies and Section 3 discusses sampling methods and semi-supervised learning. Section 4 discusses specific methods employed in this analysis and Section 5 analyses gathered data and addresses each research question in turn. Finally, section 6 summarises and concludes the paper, and discusses implications for the findings in real-world applications.

Related Work

Financial literacy studies

The financial literacy literature can be categorised in two ways. The first category explains different FL survey generation, and the second concentrates on measuring FL. Most of the research makes use of surveys to evaluate and predict the FL level. Several large-scale surveys have been conducted aimed at establishing the distribution and levels of FL.

Worthington [START_REF] Worthington | Predicting financial literacy in australia[END_REF] examined FL across 924 individuals, largely students at 14 different colleges, and associated their scores with socioeconomic and demographic characteristics. They built a database from respondent answers to an 80 question survey covering three main areas: mathematics literacy, money management skills, and financial competence. A logit model was employed to predict FL level effectively, but their model was most precise or accurate at predicting highest and lowest FL levels with enigmatic effects on intervening cells in the model. Their model depended on density functions, producing high-accuracy results only when predicting the lowest and highest FL levels, whereas the middle 60% responses remained unpredictable [START_REF] Worthington | Predicting financial literacy in australia[END_REF]. Experimental results showed that students with lower FL levels were likely to live in deprived areas, were unlikely to be business majors, and did not have much work experience. Holding all other factors equivalent or constant, older, higher educated, farm owners, business owners, and university educated respondents exhibited better FL.

Observed FL levels have decreased since the early surveys, but seem to vary between demographic and socioeconomic groups [START_REF] Lusardi | Financial literacy among the young[END_REF]. Factors that influence FL level include gender, age, ethnicity, occupation, education, income, savings, and debt. FL prediction gave high scores for professional and highly educated people aged between 50 and 60; with lowest scores for unemployed females, and those who spoke English as a second language [START_REF] Lusardi | Financial literacy among the young[END_REF].

Huang [START_REF] Huang | A financial literacy simulation model using neural networks: Case study[END_REF] proposed a back propagation neural network (BPNN) to evaluate FL level. BPNNs comprising three distinct two-hidden layer networks were employed to model credit cards, loans, and superannuation on different datasets with approximately 900 examples. Their results confirmed BPNN capability to simulate FL with 92% overall performance.

Most previous FL studies collected labelled data from questionnaire surveys, whereas the current study predicted FL level on many recorded unlabelled data from customer financial activities with only a small number of labelled data acquisitions from an online questionary.

Recent semi-supervised learning approaches

Semi-supervised learning uses unlabelled and labelled data in the learning process, in contrast with supervised and unsupervised learning methods, which use only labelled and unlabelled data, respectively. Having human annotators label data is prohibitively expensive and time-consuming, whereas unlabelled data acquisition for learning is easy and fast. However, although using unlabelled data via semi-supervised learning (SSL) is a good method to reduce human effort and improve model performance, some challenges make model tuning more timeconsuming and critical than with other machine learning techniques. Depending on the target variable type in model output, SSL can be categorised into two main approaches: semi-supervised regression (SSR) and semi-supervised classification (SSC). SSC is used where the target variable is discrete, whereas SSR is the better choice when model output is continuous.

Different SSL methods are used to fit the structure of a problem, such as maximising expectation with generative mixture models, self-training, co-training, transductive support vector machines (SVMs), and graph based methods [START_REF] Fazakis | A multi-scheme semisupervised regression approach[END_REF]. Ding et al. [START_REF] Ding | Semi-supervised learning on graphs with generative adversarial nets[END_REF] proposed GraphSGAN, applying SSL on graphs with generative adversarial networks. They experimentally confirmed the proposed approach on various datasets, including labelled and unlabelled datasets, performed significantly better than other methods, such as Chebyshev [START_REF] Defferrard | Convolutional neural networks on graphs with fast localized spectral filtering[END_REF] and graph convolutional networks (GCNs) [START_REF] Kipf | Semi-supervised classification with graph convolutional networks[END_REF], and was more sensitive to labelled data [START_REF] Ding | Semi-supervised learning on graphs with generative adversarial nets[END_REF]. GraphSGAN generated fake nodes in the density gap, reducing node influences across the density gap. Higher curvature for learned classification function around density gaps was achieved by discriminating fake from real samples. Lin and Gao [START_REF] Lin | Shoestring: Graph-based semi-supervised classification with severely limited labeled data[END_REF] proposed graph-based semi-supervised learning. They set up a shoestring framework using a typical graph based SSL, with two-layer graph convolutional neural network as a prototypical model for learning nonlinear mapping of nodes into an embedding vector, and then applied a metric learning network on the embedding vector to identify and learn pair-wise similarity between node and centroid representation in each class. The proposed method was tested on seven models and five datasets with 20 labelled data points in each class, achieving better classification performance than baseline methods.

Although the above approaches could theoretically adopt any current SSL methods, most were applied as supervised methods for classification, since realvalued target variables raise practical difficulties for SSL in regression. Motivated by these earlier studies, our methodological approach (see Section 4) is a mixed methodology SMOGN-COREG semi-supervised learning contribution to measuring FL.

Preliminary Knowledge

Sampling techniques by SMOGN

The unbalanced learning problem is concerned with learning algorithm performance in the presence of underrepresented data and severely skewed class distributions [START_REF] Vluymans | Learning from Imbalanced Data[END_REF]. The well-known synthetic minority oversampling technique for regression (SMOTER) extends the SMOTE algorithm for regression tasks and is commonly used in pre-processing to handle unbalanced datasets by generating synthetic samples for minority classes. Torgo and Ribeiro [START_REF] Torgo | Utility-based regression[END_REF] defined a relevance function to determine normal and rare value sets and map them onto a relevance scale between 0 and 1, representing minimum and maximum relevance, respectively.

A threshold t R was established on relevance values assigned to each user to define the rare value set as

D R = {|x, y| ∈ D : φ(y) ≥ t R }
and normal cases as

D N = { x, y| ∈ D : φ(y) < t R } , where D is a training set D = { x i , y i } N i=1
with N data points. The relevance function and t R are used to determine D R and D N sets in all sampling strategies.

Branco et al. proposed SMOGN [2]

, combining one random under-sampling and two oversampling techniques to increase data generation diversity, which cannot be achieved using only introduced Gaussian noise. SMOGN generates new synthetic data with SMOTER, which selects k-nearest neighbors (k-NN) based on the distance between two data points or introduces Gaussian noise [START_REF] Branco | Ubl: an r package for utility-based learning[END_REF]. SMOTER uses t R to determine whether neighbors are within safe or unsafe zones by calculating half the median distance between two data points. The main strategy is to classify important and less important cases in BinsR and BinsN partitions, and then apply oversampling and random under-sampling.

Figure 1 shows a SMOGN synthetic instance for seed cases with five nearest neighbors. Three neighbors are within the safe distance and the other two are at unsafe distance. This synthetic example shows that instances belonging to the normal bin (green) are more likely to overlap with instances associated with the relevant bin within the unsafe distance. Thus, SMOGN generates new synthetic examples and SMOTER selects K-NN or Gaussian noise based on the distance between the data points. If the neighbour is within a safe distance, it is suitable to conduct interpolation via the SMOTER method. On the other hand, if the selected neighbour is located in an unsafe zone introducing Gaussian Noise is a better selection to generate a new instance.

Co-regression semi-supervised learning

Co-regression (COREG) [START_REF] Zhou | Semi-supervised regression with co-training[END_REF] is a nonparametric multi-view and multiple learner SSR method. The flexible COREG SSL algorithm implements two regressors, one labels unlabelled data for the other, and labeling confidence for unlabelled data is determined by the sum of the mean squared error reduction over the labelled neighborhood for that data point. Final prediction is made by averaging regression estimates generated by both regressors. COREG uses a lazy learning method including two k-NNs, which improves computational load because the lazy learner does not hold a separate training phase and refine regressors in each iteration. In contrast neural networks or regression trees require many labeling iterations, with consequently heavy computational load [START_REF] Zhou | Semi-supervised regression with co-training[END_REF].

COREG employs two k-NNs to compute mean squared error (MSE) for each X u to identify the most confidently labelled data by maximising

∆ xu = xi∈Ω (y i -h (x i )) 2 -(y i -h (x i )) 2 , (1)
where h and h are the original and refined k-NN regressors, respectively; and Ω is the set of k-NN labelled points from X U . Information provided for regressors (x u , ŷu ) by where ŷu defines with ŷu = h (x u ).

Methodology

This section discusses proposed SMOGN-COREG semi-supervised learning model techniques for regression. When selecting an SSR algorithm for labeling unlabelled data, we need to consider an algorithm with low computational load and superior results on large unbalanced datasets.

Problem Statement

Depending on the model application, SSL can be categorised into inductive and transductive frameworks. Inductive semi-supervised learning can handle unseen data, whereas transductive learning only works on labelled

{(x i , y i )} l i=1 iid ∼ p(x, y)
and unlabelled

{x i } l+u i=l+1 id ∼ p(x)
training data, where L and U are labelled and unlabelled data, respectively; X is an input data point, y is a target label, P (X, y) is the unknown joint distribution, p(X) is marginal (typically p(X) = l u). The transductive method is only interested in labelled data [START_REF] Kostopoulos | Semi-supervised regression: A recent review[END_REF] {x i } l+u i=l+1 . The proposed SSL method can be expressed as

X = (x i ) i∈[n] ,
where n is total number of instances and x is independent predictor into labelled set X l = (x 1 , ..., x l ) associated with labelled data Y l = (y 1 , ..., y l ) and unlabelled instances X u = (x l + 1, ..., x l + u ), where labelled data are not available.

This paper focused on SSR because target value FL is continuous, and we employ SMOGN sampling on all datasets to balance target variable distributions.

SMOGN-COREG semi-supervised regression

We applied SMOGN for pre-processing before the learning phase to improve model performance. The SMOGN regression technique combines under-sampling the majority class (values usually found near the mean for a normal distribution in response variable y) and oversampling the minority class (rare values in a normal distribution of y, typically found at the tails). SMOGN uses a synthetic minority over-sampling technique for regression, with the additional step of using Gaussian noise to perturb interpolated values. SMOGN applies a function φ to the dependent variable, generating corresponding φ ∈ [0, 1] for each value to decide whether an observation is in the majority or minority depending on t R (defined in the arguments).

Synthetic values in categorical features are created by randomly selecting observed values contained within their respective function. After post-processing, SMOGN returns an updated data frame with under and oversampled (synthetic) observations. Figure 2 show the model process workflow comprises four main steps:

1. input data; 2. pre-processing: applied data cleansing, feature selection, and sampling; 3. labelled set data augmentation: semi-supervised regression; and 4. output.

The sampling strategies improved learning performance by increasing the small number of important rare cases. We used COREG for different distance metrics rather than requiring sufficient and redundant views. COREG has broad applicability and can successfully use unlabelled data to boost regression predictions. Thus, combining sampling strategies with a non-parametric multi learner semi-supervised regression algorithm considerably improves performance on unbalanced datasets. Both base regressors are co-trained on the primarily labelled set with size

R = |L| |D| , (2)
where D = L ∪ U is the total dataset, |L| |U |; L is the initial training set, and U denote the initial unlabelled set.

Most focus for unbalanced domains is related to classification problems, whereas research into learning algorithms are less often explored to deal with unbalanced regression. Many important real-life applications, including the economy, crisis management, fault diagnosis, and meteorology, require predicting underrepresented data and important continuous target variables. Important rare cases often accompanied by a plethora of common values cause abnormal behavior for unbalanced learning scenarios [START_REF] Krawczyk | Learning from imbalanced data: open challenges and future directions[END_REF].

Unbalanced datasets in regression cause more difficulty than in classification because the number of values can be practically infinite for continuous target variables. Prediction performs more poorly when important data points are poorly represented, and the target variable is distributed on unequal user preferences compared to distributions with more frequent data points [START_REF] Branco | SMOGN: a pre-processing approach for imbalanced regression[END_REF].

Experimental Procedure and Results

Datasets

This study obtained five datasets obtained from a superannuation company for real-world experiments to verify the proposed models effectiveness. The various datasets including considerable skewed data due to the high cardinality ratio. We intended to use balanced datasets and hence obtain reliable evaluation results. Table 1 lists the five unbalanced datasets and their attributes. CFS 2017-2018 FL contains 68 features (54 integer and 14 real variables) and 931 instances, and the other four datasets contain with 89 features (54 integer, 16 polynomial, and 19 real variables) with approximately 900 labelled and unlabelled data points, for members holding accounts with Australian superannuation company Colonial First State (CFS). Dataset features included customer financial activities, demographics, income, account balance, marital status, age, employment, and some private features used in customer relationship management teams. We added the target variable "Financial literacy" in final dataset separately after Extract, Transform, Load (ETL) data from various sources, where FL value was derived from CFS online survey questionnaire in 2017 and 2018. However, the number of customers that participated in this survey was only approximately 5% of the whole population, i.e., approximately 5% of the data was labelled, and the rest remained unlabelled in all five datasets.

Baseline and regression method configuration

The regression methods were implemented using the Weka3 platform, and we compared proposed SMOGN-COREG model performance with the following supervised and semi-supervised models.

-Linear Regression (LR) is the most popular method when there is a linear relationship between two features. We used Akaike information criterion (AIC) for model selection, and LR can deal with weighted instances. k-NN using Euclidean distance, where K = k ∈ {4, 7, 9}.

-Sequential minimization optimization (SMOreg) to implement SVM with regression using a polynomial kernel with batch size = 100. -M5 Rules model tree in if-then form, with minimum instances per leaf = 4.

-M5 Model Trees, a well-known model tree algorithm in Weka tools, constructs multivariate linear regression trees, with minimum instances per leaf = 4. -Random Forest (RF) with tree depth = unlimited, and iteration and batch size = 100. -Meta multi-scheme SSR algorithm (MSSRA) [START_REF] Fazakis | A multi-scheme semisupervised regression approach[END_REF] as the baseline model. MSSRA used three k-NN base regressors (3,7,9 k-NN), followed by self-training to enhance the labelled set by exploiting the unlabelled set and one final random forest regressor deployed for retraining after iteration. Labels for unknown test instances were then exported. Since the algorithm uses different regressors outside the iterative process, it can be considered a diversity booster, confirmed in the experiment by its robust results.

Several more supervised regressors were utilised for comparison purposes, but we did not include them the model comparison due to their considerably unsatisfactory performance.

Experiment setup

We initially employed cross-validation with 10 folds of the datasets, one fold for the test set and the remainder for learning. Unlabelled ratio UR = 80% was used to split the training set in each fold, i.e., only 20% labelled data were involved in learning. COREG maximum iterations = 100, U pool size = 100, and always ∆ xu > 0 in each iteration, hence maximum labeling capacity = 50000 iterations. However, this capacity was somewhat optimistic considering the negative impact from noisy data in the L subset. Given the labeling capacity of the algorithm, all unlabelled data in the five datasets would be evaluated for labeling well before the maximum number of iterations was reached. Experimentally trading off between iterations and model runtime, we found 500 iterations and U pool size = 100 unlabelled data was optimal and covered all confident predictions to enhance the labelled set during learning on all five datasets. We set the distance order for the two k-NN regressors in COREG as K = 2 and 3, respectively; with K = 2 for the SMOGN algorithm oversampling, and t R = 0.25. Gaussian noise introduced in SMOGN = 5%, hence perturbation = 0.05 and maximum iteration = 1000. The pool contained 100 unlabelled examples randomly selected from the unlabelled set in each iteration. The final prediction outcome is the average regression predictions for the two regressors. Average MSE was recorded for labeling most confidence instances.

We compared the proposed approach with one SSR algorithm and five widely used supervised regressors on five different datasets (see Table 1). We considered four well-known evaluation metrics to determine regression performance:

-R-squared (R 2 ) (3), -Pearson correlation coefficient (P CC) (4), root mean squared error (RM SE) (5), and mean absolute error (M AE) (6); Which can be expressed as

R 2 = 1 - RSS T SS , (3)
P CC = n i=1 (y i -ȳ) (y i -ȳ ) n i=1 (y i -ȳ) 2 (y i -ȳ ) 2 , (4)
RM SE = 1 n n i=1 (y i -y i ) 2 , (5)
M AE = 1 n n i=1 |y i -y i | , (6)
respectively, where the dataset has n values {y 1 , ..., y n } with y i values are real in a multivariate linear model with

Y i = β 0 + P j=1 β j X i,j + ε i ,
y corresponding to the predicted value for data point x i , ȳ is the mean of the observed data; ȳ, ȳ are mean values for y i and y i , respectively. Larger P CC and R 2 and smaller M AE and RM SE represent improved prediction accuracy.

Empirical Results

Based on the aim of achieving excellent results from SMOGN for tackling imbalanced regression problems, an experiment was conducted on the main dataset CFS 2017-2018 FL and its results presented in Figure 3 show a clear pattern of the imbalanced target variable distribution, with a higher density over 0.5, and vice versa. The dark blue histogram shows that after applying SMOGN, fewer sample data points were extracted between the values of 0.6 and 0.9. In contrast, some extra samples were generated from lower than 0.5 values. Thus we can claim that the skewed data distribution was modified after applying SMOGN.

On the other hand, this improvement in data distribution led to boosting the COREG learning capability to generate higher accuracy in minority samples. It concludes that the mixed methodology of oversampling and undersampling by the SMOGN and COREG is worked efficiently not only in our targeted dataset and other similar finance datasets. The experimental results on main dataset "CFS 2017-2018 FL" presented in the tables and graphs prove that the proposed model performed better than the other regression algorithms. Specific results of the aforementioned evaluation metrics on five different datasets are as follows:

-The RMSE results shown in Table 2. are in an acceptable range for the proposed model with minimum RMSE 0.1285. The M5 had the best result with the lowest RMSE at 0.1207; in contrast, the LR indicated poor performance with the highest RMSE of 1.1528. On the other hand, the data analysis in Table 2 showed that the lower fluctuation level is obtained in the proposed SMOGN-COREG and RF model with a standard deviation of 0.0051 and 0.0019, unlike the SMOreg and LR 0.023592117 and 0.459051896, respectively. This finding confirms additional evidence that although the RMSE result in the SMOGN-COREG -In addition to the above results, it should be noted that these findings cannot be extrapolated to all type of datasets structures. The max cardinality of Fig. 6: The above graph shows the correlation of evaluation metrics results with UR.

the dataset "CFS 201706" is significantly larger than other ones. Namely, the large number of unique values led to a low strength relationship between data points. As shown in RMSE results in Table 2. and PCC results in Table 3. the supervised model M5 performs better on the dataset "CFS 201706".

Therefore, from the final experiment, given the ability to predict the FL of customers, we can conclude two things: First, our hypothesis using a combination of sampling strategy techniques with a nonparametric multi-learner SSR algorithm provides better results than other regressors on imbalanced financial network data. Second, overall, exploiting a large amount of unlabelled data via SSR methods improves prediction accuracy more than using only labelled data in supervised methods on data collected based on customer financial activities. In Figure 5, the MAE in SMOGN-COREG improved 50% over the COREG algorithm and 7.3% over the baseline model. In Figure 4. SMOGN-COREG improved the R-squared 67% and 18% over the COREG and baseline algorithms, respectively.

The different ratio of the size of the unlabelled dataset to the total amount of data, called Unlabelled Ratio (UR), affects the R-squared, RMSE, and MAE. Figure 6. shows that the R-squared was imroved 31% by increasing the number of unlabelled data points. The MAE was worse when learning was trained with a 70% unlabelled ratio. Moreover, the RMSE value improved 9.8% with a 99% UR. Monitoring the model performance via the UR can be helpful for designers who want to simulate their own model output based on a different UR. The python implementation for the proposed model and result visualisation is available in GitHub (https://github.com/DavidHason/predicting-financial-literacy), to simplify reproducing and improving this study experiment results.

Conclusions

Irrational financial decisions can have irreversible impacts on quality of life. Many relevant reports and articles have shown that people with poor FL are considerably more vulnerable to social harm and financial losses, such as job loss; family loss; reduced life expectancy; mental health problems; and most importantly, low-income retirement. To prevent this, it is essential to estimate FL and hence allocate specific intervention programs and financial advice to less financially literate groups. This will not only increase company profitability but also reduce government spending. Considerable financial data is recorded in the contemporary world, with a high proportion of that data unlabelled. Therefore, it is impossible to include this massive data repository in predictive models. The primary purpose for the current study was to develop a suitable method to predict FL level using financial datasets, which often include considerable unlabelled data.

Many SSL techniques have been used for various real-world applications, including GCNs, self-training, and co-training. Empirical results confirmed that combining SMOGN and COREG algorithms on unlabelled data reduced cost and model runtime, and improved prediction accuracy beyond current supervised regression methods. Most real-world problems involving unlabelled examples analysed using SSR methods have better prediction accuracy than using only labelled data in supervised methods. Thus, this study results represent a further step towards applying SSR techniques to assist FinTech companies in narrowing their consumer financial behavior and targeted marketing campaigns.

The proposed solution was based on an offline learning process because the proposed statistical predictive method was applied to previously collected data. Future study will investigate implementing online learning on streaming data. This would provide a predictive engine to conduct more accurate and trustworthy predictions, with additional data potentially coming from other financial institutions, such as other superannuation or FinTech companies. Using an active learning algorithm in the SSR models could be a potential method to achieve this.

Acknowledgement

This work is partially supported by the Australian Research Council (ARC) under Grant No. DP200101374 and LP170100891.

Fig. 1 :

 1 Fig.1: Synthetic examples in SMOGN[START_REF] Branco | SMOGN: a pre-processing approach for imbalanced regression[END_REF]

Fig. 2 :

 2 Fig. 2: Proposed SMOGN-COREG model workflow

Fig. 3 :

 3 Fig. 3: Distribution of target variable before and after applying SMOGN on CFS 2017-2018 FL dataset

Fig. 4 :

 4 Fig. 4: The proposed SMOGN-COREG model is obtained the best R-squared.

Fig. 5 :

 5 Fig. 5: The SMOGN-COREG and M5 models achieved the better MAE result.

Table 1 :

 1 Datasets collected

	Dataset	# Attributes # Instances Size
	CFS 201706	89	824	73336
	CFS 201712	89	856	76184
	CFS 201806	89	899	80011
	CFS 201812	89	918	81702
	CFS 2017-2018 FL	68	931	64239

Table 2 :

 2 RMSE results of supervised regressors, baseline MSSRA and proposed SMOGN-COREG model SMOreg and LR because the two SMOGN-COREG regressors train on the augmented training set built by the combination of the initial labelled set and Pseudo-code subset increased model prediction error in SSL in compare with supervised learning methods due to the inherent limitations of SSL, the amount of noise in data generate many incorrect pseudo-labels, leading to erroneous high confidence predictions. Despite this, the SMOGN-COREG algorithm's stability is higher than the baseline SSL algorithm, and other mentioned supervised learning models in the finance datasets. -A significant improvement in R-squared and PCC values with our proposed model demonstrates the compatibility of the two algorithms, SMOGN and COREG, on the financial dataset. Moreover, the results emphasise the im-

	Datasets	CFS 2017-2018 FL CFS 201812 CFS 201806 CFS 201712 CFS 201706
	MSSRA	0.1367	0.1565	0.1618	0.156	0.1549
	SMOGEN-COREG	0.1356	0.1335	0.1303	0.1285	0.1416
	4-NN	0.1344	0.1483	0.1581	0.1502	0.1513
	7-NN	0.1306	0.1439	0.153	0.1447	0.1465
	9-NN	0.1284	0.1426	0.1533	0.1448	0.1462
	SMOreg	0.1317	0.1321	0.1325	0.1831	0.1263
	LR	0.1275	0.1224	0.1304	1.1528	0.1251
	M5	0.1276	0.1214	0.1223	0.1227	0.1207
	M5rules	0.1277	0.1215	0.1229	0.1231	0.1208
	RF	0.1317	0.1339	0.1359	0.1362	0.1361
	is slightly higher than supervised models M5, portance of exploiting the sampling technique SMOGN to improve model
	performance on imbalanced datasets. The R-squared and PCC results in
	Figure 4. and Table 3. show that the COREG algorithm achieved the lowest
	R-squared and PCC results at 0.4431 and 0.6656, respectively, in contrast
	to SMOGN-COREG, which obtains superior results of 0.7171 and 0.8468,
	respectively.

Table 3 :

 3 PCC results of supervised regressors, baseline MSSRA and proposed SMOGN-COREG model

	Dataset	CFS 2017-2018 FL CFS 201812 CFS 201806 CFS 201712 CFS 201706
	MSSRA (Base-model)	0.7922	0.7465	0.7322	0.7476	0.7501
	SMOGEN-COREG	0.8468	0.8384	0.8622	0.8523	0.7454
	4-NN	0.7876	0.7274	0.6837	0.7171	0.7095
	7-NN	0.7988	0.7472	0.7117	0.7442	0.7325
	9-NN	0.806	0.754	0.7144	0.7461	0.7365
	SMOreg	0.7955	0.7919	0.7917	0.6295	0.8085
	LR	0.8092	0.8249	0.8025	0.0858	0.8134
	M5	0.8094	0.827	0.8259	0.8225	0.8268
	M5rules	0.8092	0.8267	0.824	0.8214	0.8265
	RF	0.7985	0.8119	0.8081	0.809	0.8053
	Improved %	4.6	1.4	4.4	3.6	-8.8

Weka is a collection of machine-learning algorithms for data mining tasks in the Java SE platform, operating Windows, OS X, Linux