Sami Makki

Chiara Ruffa

Annick Wibben

Anne A Lake

Talent

Eric Reid

Solomon Sonya

Stephanie K Erwin

Heiko Biehl

Samantha Davis

Rachel Sondheimer

Andrew Pfluger

Anne B Lake

Supports

Nathan Toronto
email: nathan.w.toronto.civ@mail.mil

Pascal Vennesson

S Rajaratnam

Patricia Shields

Donald S Travis

Todd Woodruff

Ryan Pallas
email: rpallas@gmu.edu

Maria Sabrina

Erica Carlos

Leslie V Advincula-Lopez

Risa Brooks

II. B. Achievements: a unique program to implement civmil relations through higher military Training and Education of future military leaders

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Teaching (online) general culture/knowledge to French military officers competing for the National War College: telling and analyzing a unique and challenging experience

Sami MAKKI

Sciences Po Lille and CERAPS

Sami.makki@sciencespo-lille.eu The purpose of this paper is to recount, contextualise and analyse (for the first time with a non-Frenchspeaking audience) a personal experience of eight years as the national coordinator in charge of the official preparation of French military officers for the competitive entrance examination to the National War College (the equivalent of the UK's Joint Services Command and Staff College), and in particular the (much dreaded) 'general culture/knowledge' essay ('l'épreuve de Culture Générale'). This training programme is based on an initial 4-year contract with the French Directorate of Higher Military Education (DEMS).

It is therefore a set of training activities organised by the French Ministry of Defence for the benefit of all officers of the French Armed Forces in order to gain access to higher positions in their career. But the selection process is particularly rigorous, with written tests such as the military briefing ("note de synthèse") and the general culture/knowledge essay, which we will discuss in more detail.

THE METHODS + General knowledge

For an academic, the first years of learning this military culture and a certain approach to the official "methodology" were a new and challenging dialogue with practitioners, different from the existing relationships established a few years earlier during doctoral work.

As a senior lecturer, I was asked in 2015 to set up a partnership to offer an innovative approach to this distance learning programme by giving it an original civil-military dimension. Using a series of human and intellectual experiences and events as examples, we will describe the stages, developments and transformations of this project, drawing lessons from the difficulties encountered and the pedagogical successes.

Beyond the editorial stakes, it will be important to explain how the civilian and military leaders of the institution define and understand the importance of a "general culture" considered essential for the quality training of the future military leaders of French democracy.

The purpose of this paper is to give an initial assessment of a unique experience for an academic, which was both rich in lessons and sometimes complex to manage.

OBJECTIVES AND SPIRIT OF THE "GENERAL CULTURE" TEST (GENERAL KONWLEDGE ESSAY)

The written General Culture test (essay) for the Ecole de Guerre entrance exam is officially defined as follows:

"Lasting four hours, this test is designed to:

-assess the candidate's ability to reason, to support a thesis in response to a question.

-judge the quality of written expression.

-assess the candidate's knowledge of contemporary issues.

The emphasis is on the ability to analyze and synthesize, and on argumentation skills rather than encyclopedic knowledge."

This very selective test (a competitive examination) is a blend of international relations, history, philosophy, and social science analysis of major contemporary issues.

TEST DEFINITION

There are three imperatives:

-Demonstrate logical thinking

The general culture essay is above all an exercise in reflection.

The essay is not an exposé. It is not skeptical, but it must be convincing. It avoids displaying knowledge, developing quotations or examples for their own sake, summarizing reading notes or repeating a lecture. Just as carefully, they avoid clichés and platitudes, cultivating instead the intelligence of the message and the finesse and nuance of the demonstration.

-Arguing and convincing

The general culture essay is a reasoned response to a question posed by the subject. This response must be rigorous and methodical, based on a well-constructed approach and an organized way of thinking. Method is not an end in itself, but a tool for organizing a demonstration. It must convince the reader of the accuracy of the ideas expressed, thanks to the presentation of solid, well-thoughtout arguments.

-Mobilize all the resources of your general knowledge

The general culture essay is a line of reasoning underpinned by personal general culture. It is based on two principles: logic and discernment. The reasoning chosen must follow a logical order in the coherence of the ideas developed, which form a whole articulated in several stages. The choice of arguments presented reveals the writer's discernment. It's not about asserting personal convictions, which, however respectable they may be, are often not enough to win support. The aim of the essay is to win people over, not to show off one's more or less well-established knowledge.

The trainer/examiner will value a clear, straightforward approach that uses a wealth of examples to demonstrate a form of conviction.

EVALUATION CRITERIA

The assessment of a general knowledge essay is not based on the instructor's subjectivity. He or she takes into account three fundamental criteria: the treatment of the subject, the quality of the demonstration and respect for the formal rules attached to this exercise. Here are the main questions to be answered:

-Is the subject understood?

-Precise answer to the question posed.

-Understanding of the underlying issues.

-Delimitation of areas for reflection...

-Are the ideas well formulated?

-Clarity of formulation.

-Quality of thought.

-Appropriateness to subject ...

-Is the reasoning coherent and balanced?

-Logical articulation of arguments.

-Hierarchy of ideas in ascending order of interest and relevance ...

-Are the references and examples cited relevant?

-Sufficient degree of precision and variety.

-Consistency of ideas / references and examples ...

-Is the formal quality of the assignment satisfactory?

-Careful presentation.

-Fluidity and conciseness of style.

-Accuracy of vocabulary.

-Correctness of language ... Finally, in the context of a competitive examination, grading remains relative and is primarily intended to eliminate candidates. This means that any negligence concerning, for example, the quality of general presentation, handwriting, punctuation, spelling and syntax can result in an eliminatory mark.

III. Lessons to be learned and reflections about the future of PME and civil-military relations in France III.A. Taking stock of 8 years off the straight and narrow: my way of the cross.

• An extraordinary, enriching experience, and a unique civil-military experience sometimes misunderstood by some and others. • An apparent and noticeable improvement in thinking about societal issues.

• A permanent power struggle between academic logic (critical reflection and in-depth questioning)

and the imperatives of military culture (getting straight to the point in simple terms and sometimes with simplistic analysis).

III. B. An unhealthy relationship despite a few recent initiatives (AEGES, ACADEM).

• Impossible university recognition or even a false perception of this experience.

• The army-nation link is being abused.

• The reality of tensions in 2023, with a strong military hold in a context of instability + a form of lowkey militarization of minds (cf. British context). • Academics and general officers: mutual respect... and a tight smile? III. C. Worrying securitization of the minds (reinforces the absence of democratic debate forcing a soft consensus) ?

• Series of crisis management failures on the ground and inability to anticipate changes in the Sahel.

Sign of a clear dysfunction and militarization of the French EP in Africa. • The Knowledge and Anticipation function is attractive, but disappointing.

• The Red Team project (see Éric's conclusion) in relation to anticipation: the scientific guarantee. Eric Sangar's next article focuses on the puzzle of the French Red Team: from a military threat anticipation project launched in 2019 with the intention of contributing to the technological and capability innovation of the armed forces, the French Red Team has become a vast media operation whose scenarios contents and especially the public relations activities targeting the general public seem to have little to do with the original objectives.

• Exchanges are too often one-way, reinforcing a sense of mistrust.

Conclusion

We need to transform these relationships by increasing the number of encounters, so as to better understand and respect each other. For the social sciences in France, there is a real challenge, as these fields of knowledge are also perceived as falling within the remit of Army officers (Saint Cyr, etc.).

There is a need to make these experiences better known and to enable other colleagues to get involved in these initiatives to improve processes. But this can have disastrous consequences on an academic career (no more time for research, which is already greatly reduced by teaching hours and excessive administrative tasks). Among many colleagues in the social sciences, there is still a great deal of confusion and suspicion that academics are compromising themselves to the "militarist lobby". I've just fallen victim to this again... after 15 years in the institution. There's still a long way to go...

IUS International

FIRST

  Teaching and Learning in the School House Chair: Sami Makki, Sciences Po Lille, France

  of Maryland Suzanne Nielsen, United States Military Academy Peter Feaver, Duke University Anthony King, Warwick University, United Kingdom Panel 46: Grand Ballroom B. Gender Integration in the Armed Forces II Chair: Sara Simmons, Georgetown University School of Continuing Studies, svsimmons@hcvantage.net Discussant: Hannah West, Cardiff University, United Kingdom, WestH2@cardiff.ac.uk Who's Fit to Serve? Unpacking Soldiers' Gendered Perceptions of the U.S. Army Combat Fitness Test Carrie Carter, North Carolina State University The Untold Queer History of the Naval Academy Caroline Bilbray-Kohn, United States Naval Academy Sociology in Action: Designing Policy Recommendations for the U.S. Marine Corps to Increase Gender Integration at Recruit Training Sidra Montgomery, Westat Insight 3:00-3:15 pm

Table of content (See PPT for illustrations) Final conclusion

  ..................................................................................................................................

I.

My PME Experience in the French CMR context

  ................................................................................. I. A. The lack of coherence at the institutional level and the limited role of expert networks in designing new civil military relations ................................................................................................................... I. B. The need for accountability in threatened democratic spaces. ...................................................

II.

Responsibilities away from teaching and Research

  ........................................................................... II. A. The CONTRACT ........................................................................................................................ -Implementing a unique relationship ... with little transparency ....................................................... -Overview of organization chart and scope of missions ..................................................................... Conclusion ............................................................................................................................................ References .........................................................................................................................................

-General culture/knowledge for officers of the French armed forces ................................................ II. B. Achievements: a unique program to implement civmil relations through higher military Training and Education of future military leaders .................................................................................................... -Resisting a pace away far from university logic: ................................................................................ -Stimulating challenges ....................................................................................................................... III. Lessons to be learned and reflections about the future of PME and civil-military relations in France .................................................................................................................................................................. III.A. Taking stock of 8 years off the straight and narrow: my way of the cross. ................................. III. B. An unhealthy relationship despite a few recent initiatives (AEGES, ACADEM). ......................... III. C. Worrying securitization of the minds (reinforces the absence of democratic debate forcing a soft consensus) ? .........................................................................................................................................

Conference Friday 13 -Sunday 15 October 2023 Final Program Version: October 13, 2023 Voir Pages 16 et 27 du programme final DRAFT PROGRAM FRIDAY 1 Friday 13 October 8:00-9:30 am Panel 1: Lake Fairfax A. School of the Democratic Nation: Postsecondary Education of Citizen- Officers across Allied and Partner Countries Chair

  : Matias Ferreyra, Texas State University Discussant: Chuck Allen, United States Army War College, charles.allen@armywarcollege.edu West Point: Behind the Building of Leaders of Character Archie Bates III, Russell Lemler, and Morten G. Ender, United States Military Academy at West Point The Challenges of Education for Military Officers in Spain: Diversification and Intensification Rafa Martinez, University of Barcelona Options, Controversies, and Debates for Pre-Commissioning University Education at Canada's Military Colleges Christian Leuprecht, Royal Military College of Canada Military Education and Training and the Military Setting in Jamaica Othneil Blackwood, Jamaica Defence Force Kevron Henry, Jamaica Defence Force Comprehensive Faculty Development for the Whole Student Approach to Higher Education: A

	Service Academy Model
	Rachel Milstein Sondheimer, United States Military Academy
	Andrew Pfluger, United States Military Academy
	Benjamin Wallen, United States Military Academy
	Aryn Pyke, United States Military Academy
	Military Education in Switzerland
	Stefano De Rosa, Military Academy ETH Zurich, Switzerland
	Panel 2:

Lake Anne A. Special Operations Forces in the 21st Century Chair

  : Michael Noonan, FPRI/American University, mnoonan@fpri.org Discussant: Stephen M. Grenier, Johns Hopkins University, sgrenier@jhu.edu Footprint Size in Partner Warfare: Impact on Building Partner Force Capacity Kyle Atwell, Irregular Warfare Initiative The Special Operator and the State: Considering the Civil-Military Relations of Special Operations Zach Griffiths, Irregular Warfare Initiative SOFs Role in Resilience and Resistance Building in Nations Vulnerable to Invasion/Loss of Sovereignty Shannon Houck, Naval Postgraduate School Understanding US Special Operations Command's Global SOF Network Stephen M. Grenier, Johns Hopkins University US Special Forces Transformation: Post-Fordism and the Limits of Networked Warfare Patrick Bury, University of Bath, United Kingdom

Panel 3: Lake Anne B. Service Member and Family Resilience and Well-Being Chair

  : Katie E. Matthew, United States Military Academy Discussant: Cutting the Gordian Knot of Warfighter Development: Ethics, Lethality, and Moral Injury Prevention Dave Cotting, Institute for Defense Analyses Military Life Challenges Among Military Spouses: Implications for Future Interventions Bianca Comicho, Department of Social Welfare and Development, Mabini Extension, Philippines An Assessment of the Overlap Between Morale and Resilience in a Pre-Deployment Military Sample: A Self-Determination Theory Approach Matthew Huebner, Department of National Defence, Canada Kathy Michaud, Department of National Defence, Canada Laura Seidel, Department of National Defence, Canada Moral Injury and Encounters with Child Soldiers: Results from the 2022 Your Say Survey Stefan Wolejszo, Department of National Defence, Canada Dialogues and moral injury: the Camino de Santiago and healing Frederick Dotolo, St John Fisher University

Panel 4: Grand Ballroom A. Who Serves and on What Terms? Veteran Dialect, Military Exceptionalism, and Reproductive Rights

	Chair: Nick Mararac, University of Alaska Fairbanks, nick@nickmararac.com
	Discussant:
	The Veteran Dialect: Discourses of Identity in the Liminal Space between Institutions and
	Ideologies
	Sarah Clark, United States Army Corps of Engineers
	Queering the Military: Thank You for Your Service
	Nick Mararac, University of Alaska Fairbanks
	Roe v. Wade and the U.S. Military: A Year Review of Ongoing Battles for Service Members'
	Reproductive Rights
	Leandra Hernandez, Utah Valley University
	Victoria McDermott, University of Alaska Fairbanks
	Amy May, University of Alaska Fairbanks
	Panel 6:

Grand Ballroom B. International Response to/for Ukraine

	9:30
	Chair:
	Discussant: Timo Graf, Center of Military History and Social Sciences of the Bundeswehr,
	Germany, TimoGraf@bundeswehr.org
	Motivated to Fight: Examining Foreign Fighters Motivation to Join the Ukrainian Legion
	Ori Swed, Texas Tech University
	Aria Stiefelmaier, Texas Tech University
	Oleksandra Hanchukova, Texas Tech University
	Public Opinion in Southeast Asia on Japan's New Security Cooperation
	Saya Kiba, Kobe City University of Foreign Studies, Japan
	Revisiting the Synthetic Organization: a Pivot Between Chaoplexic Warfare and Bricolage
	Guido van Os, Netherlands Defense Academy
	Eric Hans Kramer, Netherlands Defense Academy
	Stefan Soeparman, Netherlands Defense Academy
	Robbert Verhulst, Netherlands Defense Academy
	Lithuania's Civil-Military Crisis as a Sign of Maturing Democracy
	Deividas Slekys, Vilnius University, Lithuania

-10:00 am BREAK Friday 13 October 10:00-11:30 am Panel 7: Lake Fairfax A. Developing Strategic Leaders to Face the Challenges of the Strategic Environment

	Chair: Chuck Allen, United States Army War College
	Discussant: Othneil Blackwood, Jamaica Defense Force, othneil.blackwood@jdf.mil.jm
	Using Reflective Practices to Enhance Online Education
	Heather Smigowski, United States Army War College
	Strategy, Ethics, and Due Diligence
	Celestino Perez, United States Army War College
	A Tale of Two Colleges: A Comparative Analysis of Professional Military and Civilian Executive
	Education
	Fredric Maddox, United States Army War College
	Assessing the Health of the Institution of Command
	Thomas Galvin, United States Army War College
	Developing Leaders for Disruptive Change: An Inclusive Leadership Approach
	Sara Simmons, Georgetown University School of Continuing Studies
	Panel 8:

Lake Anne A. Heroism in a Post-Heroic Era

  Laugen, The Norwegian Institute of Defence Studies Legitimate Use of Violence? Interrogating the Hero-Soldier of the Marawi War in the Philippines Rosalie Arcala Hall, University of the Philippines Visayas Duvince Zhalimar Dumpit, University of the Philippines Visayas The Warrior Archetype Revisited from a Jungian Perspective Eric Ouellet, Canadian Forces College

	Chair: Discussant: Peter White, Auburn University, pbw0011@auburn.edu Leadership, Not Sacrifice: Comparing US, UK, and Norwegian War Decorations from the War in Afghanistan Patrick Bury, University of Bath Anthony King, King's College London Torunn Haaland-The Professional Hero Panel 9:

Lake Anne B. Gender Integration in the Armed Forces I

  Chair: Sandra Biskupski-Mujanovic, University of Waterloo, Canada, Sandra.biskupskimujanovic@msvu.ca Discussant: Christina Gregory, Lamar University, cgregory7@lamar.edu State, Military and Women: Thirty Years of Women's Military Participation in the Indian Navy Meera Bhadre, SNDTWU, Mumbai Racial Differences among Military Women: During and After the Cold War Brenda Moore, University at Buffalo (SUNY) "Don't Draft Our Daughters": Women, Citizenship and Conscription in Modern America Bradford Wineman, United States Marine Corps Command & Staff College Men and Gender Equality in the Canadian Armed Forces Sandra Biskupski-Mujanovic, University of Waterloo, Canada What Explains Variations in Levels of Gender Integration Across African Militaries? Naunihal Singh, United States Naval War College

Panel 10: Grand Ballroom A. The Meaning and Dimensions of Military Service: Strategies to Address Moral Injury and Post-Service Health

	Lydia Hill, United States Air Force Academy
	Transgressive Behavior, Definitions of Female and Male Veterans Compared
	Rene Moelker, Netherlands Defence Academy
	Tessa op den Buijs, Netherlands Defence Academy
	Emiel Maliepaard, Netherlands Defence Academy
	Chair: Vincent Connelly, Oxford Brookes University, vconnelly@brookes.ac.uk
	Discussant: Dave Cotting, Institute for Defense Analyses, dave.cotting@gmail.com
	You Should Try Yoga: Understanding Suicide Among Military Service Members
	Emily Rose Kosanovich, United States Naval Academy
	Use of Online Mental Health Tools by California Veterans When Experiencing Stigma or
	Suicidality

Panel 11: Grand Ballroom B. Roundtable on Open Letter by Former Secretaries of Defense and Chairmen of the Joint Chiefs of Staff

	Chair and Discussant: Peter Feaver, Duke University, pfeaver@duke.edu
	Risa Brooks, Marquette University
	Alice Friend, American University and Georgetown University
	Ron Krebs, University of Minnesota
	Carrie Lee, United States Army War College
	Heidi Urben, Georgetown University

Panel 12: Grand Ballroom C. Perspectives on Insurgencies and Interventions: Historical and Modern

	Atsushi Yasutomi, Eikei University of Hiroshima, Japan
	11:30 am -1:00 pm
	LUNCH
	There
	Chair:
	Discussant: John Nagl, Army War College, john.nagl.civ@armywarcollege.edu
	The Russo-Japanese War and Polish Independence: Lessons for Insurgents
	Michael A. Guzik, Le Moyne College
	Naval Intervention during Non-Intervention: The Italian Navy in the Spanish Civil War, 1936-
	1939
	Frederick Dotolo, St John Fisher University,
	Reform Rebels and Civil Military Relations in Africa
	Moses Khisa, North Carolina State University
	Little Green Men, Motorcycle Gangs, and Changing Military Influence from Within
	Donald Ziolkowski, United States Marine Corps (Ret)
	The Contracted Continuum of Security Actors in Africa: From Mercenary Activity to Industry
	Support Organizations
	Whitney Grespin, Institute for Security Governance DSCA/DSCU
	Post-Civil War Civil-Rebel Relations

are many restaurants in the area from which to choose.

	11:30 am -1:00 pm
	IUS BOARD &

COUNCIL MEETING FRIDAY 13 October1:00-2:30 pm Panel 13: Lake Fairfax A. The Social Psychology of Cadets and Midshipmen Chair

  : Morten G. Ender, United States Military Academy at West Point, morten.ender@westpoint.edu Discussant: Get an Edge on Life in the Army?: A Look at GEN Z's Attitudes towards Military Service in a Volatile Economy Katie E. Matthew, United States Military Academy Nicholas A. Rich, United States Military Academy From Studying Abroad to Leading at Home: Short Multicultural Experiences Can Facilitate Leader Development Terence McElroy, University of North Carolina, Chapel Hill Todd D. Woodruff, United States Military Academy Leadership Development Through Participation in Peer Education and Peer Support Programs Andrea M. Coutoulakis, United States Naval Academy The Role of Collective and Personal Self-Esteem and Depression in a Military Context: An Update David E. Rohall, Ohio Eastern University Ryan D. Kelty, United States Air Force Academy

Panel 14: Lake Anne A. Russian Civil-Military Relations and Military Performance Chair

  : Kirill Shamiev, European Council on Foreign Relations, Shamiev_Kirill@phd.ceu.edu Common Components Analysis of Veteran Employment Programs Daniel F. Perkins, Clearinghouse for Military Family Readiness, Pennsylvania State University Brandon A. Balotti, Clearinghouse for Military Family Readiness, Pennsylvania State University Nicole R. Morgan, Clearinghouse for Military Family Readiness, Pennsylvania State University

	What are the Help-seeking Behaviors of Military Veterans Pursuing Entrepreneurship?
	Quantifying the role of mentoring relationships and business advice in veteran
	entrepreneurship ecosystems
	Michael Lotspeich-Yadao, University Illinois Urbana-Champaign
	Craig Carpenter, Michigan State University
	Charles Tolbert, Baylor University
	What Motivates Veterans to Become Small Business Owners: a Grounded Theory Study
	Joshua Fletcher, Sullivan University

Panel 17: Grand Ballroom B. Examining Institutions of Civilian Control: Cross-Regional Perspectives Chair

  and Discussant: David Pion-Berlin, University of California, Riverside, david.pion@ucr.edu Understanding the Balance: Applying the Principal-Agent Framework to the Study of Ministries of Defence Gabriel Alves Pimenta, University of California, Riverside The Battle for Control: Challenges to the implementation of civilian control over the military Andre Anselmo, Syracuse University Threat Perception and Military Professionalism: Evidence from Ukraine and Brazil Polina Beliakova, Massachusetts Institute of Technology Kai Michael Kenkel, Dartmouth College and Pontifical Catholic University of Rio de Janeiro, Brazil

	Overseer, Supporter, Protector? Examining roles defense agencies can play
	Stephen Saideman, Carleton University
	Enhancing Democratic Civilian Control and Military Effectiveness: Comparing Defence reforms
	in India and Japan
	Anit Mukherjee, Nanyang Technological University, Singapore
	Re-

Panel 18: Grand Ballroom C. Perspectives on Afghanistan Chair

  : Don Inbody, Texas State University Combat and Service: Understanding how the framing of military as 'work' impacts the categories of invalides and disabled veterans Anne-Lise Dall'Agnola, Université Paris 8, France Changing Demands and Expectations: Veteran support and care since the Second World War Lene Ekhaugen, Norwegian Defence University College Improving Access to Services for Female Veterans: Understanding challenges from serviceproviders' perspectives Rita Phillips, Alpe Adria University Klagenfurt Moira Bailey, Robert Gordon University, United Kingdom Zoe Morrison, Robert Gordon University, United Kingdom The Relationship Between Social Support and Self-Rated Health for Military Veterans Mandy McGowan, Baylor University Michael Lotspeich-Yadao, University Illinois Urbana-Champaign Veteran Stereotypes, Support for Benefits, and Endorsement of Military Service Meredith Kleykamp, RAND Corporation Daniel Schwam, RAND Corporation Gilad Wenig, University of California, Los Angeles

Panel 23: Grand Ballroom A. War in Ukraine: The Battle for Public Opinion Chair

  : Lawrence Chalmer, National Defense University Discussants: Michael St-Pierre, Canadian Armed Forces, stpierre.msp@gmail.com From Reluctance to Reassurance: Explaining the shift in the Germans' NATO alliance solidarity following Russia's invasion of Ukraine Timo Graf, Center of Military History and Social Sciences of the Bundeswehr, Germany Markus Steinbrecher, Center of Military History and Social Sciences of the Bundeswehr, Germany One Year of War in Ukraine: Changes in the Acceptance of NATO, of the Trust in Great Powers, and in the Perception of Neutrality, Among the Population of Switzerland Stefano De Rosa, Military Academy ETH Zurich, Switzerland Tibor Szvircsev Tresch, Military Academy ETH Zurich, Switzerland

	The Winning Narrative or the Narrative on Winning: Ukraine's public messaging during Russia's
	war on Ukraine
	Khrystyna Holynska, Pardee RAND Graduate School

Panel 50: Grand Ballroom B. Roundtable on Civil-Military Relations in Democracies-How Healthy are They?

	Chair: Risa Brooks, Marquette University, risa.brooks@marquette.edu
	Discussant:
	Stephen Saideman, Carleton University
	Nicole Jenne, Pontificia Universidad Catolica de Chile
	Christoph Harig, Royal Danish Defence College
	Sofia Ledberg, Swedish Defence University
	Anit Mukherjee, Nanyang Technological University, Singapore
	4:15

-4:30pm BREAK Friday 13 October 4:30-6:00 pm Panel 24: Lake Fairfax A. Challenges and Opportunities for Integrating Gender Perspectives in Military Education: Experiences from the US and Canada Chair

  : Grazia Scoppio, Royal Military College of Canada, scoppio-g@rmc.ca Discussant: Sonya Finley, National Defense University, sonya.l.finley.civ@ndu.edu Teaching Gender to Soldiers: Experiences from a Canadian Educator Grazia (Grace) Scoppio, Royal Military College of Canada and Queen's University Critical Thinking Without Critical Perspectives? Teaching/Learning Reflective and Deliberative Skills and Dispositions through the Women, Peace and Security Curriculum Mariya Y. Omelicheva, National Defense University Considerations for Working with Students Resistant to Women, Peace and Security Efforts in

	Professional Military Education
	Lauren Mackenzie, Marine Corps University
	Teaching Gender at the Royal Military College of Canada
	Sarah Hill, Royal Military College of Canada

Panel 25: Lake Anne A. Military Cognitive Skills and Decision-Making US

  Dollar Primacy in an Age of Economic Warfare Mike St-Pierre, Canadian Forces College Michael Kao, Akanthos Capital Management The Modernization of the French Nuclear Arsenal July Decarpentrie, Swedish Defence University Holding Corporations (of the new Military-Industrial Complex) Accountable for Big Data Mismanagement? Data Awareness and Public Oversight in Democratic Settings Sami Makki, Sciences Po Lille, France

BANQUET AND AWARDS CEREMONY 6:45-8:15 pm Grand Ballroom D-G

	Presenters:
	Welcome
	Ryan Burch, IUS Conference Director
	Remarks
	Laura Miller
	IUS Chair & President
	RAND Corporation
	Graduate Student Paper Award
	Brenda Moore
	Committee Chair
	University at Buffalo (SUNY)
	Charles Moskos Prize
	Patricia Shields
	Editor-in-Chief of Armed Forces & Society
	Texas State University

Morris Janowitz Career Achievement Awards

  Rene Moelker, Netherlands Defence Academy Laura Miller, IUS Chair & President Jun Yan Chang, S. Rajaratnam School of International Studies, Nanyang Technological University, Singapore Status Loss and Public Demands for Border Militarization Mike Kenwick, Rutgers University Sarah R. Maxey, Loyola University Chicago

Panel 65: Grand Ballroom C. Roundtable on Ukraine-How Does This End?

  Chair: Patrick Bury, University of Bath, United Kingdom, pb696@bath.ac.uk Roundtable Panelists: Kirill Shamiev, European Council on Foreign Relations Khrystyna Holynska, Pardee RAND Graduate School Rob Lee, Foreign Policy Research Institute

Chair & Discussant: Lindsay P. Cohn, United States Naval War College, lindsay.cohn@usnwc.edu


Keywords: Quantitative finance, risk analysis, beta, alpha, trends, technical analysis, estimation techniques, forecasting, abrupt changes, nonstandard analysis

We suggest a new model-free definition of the beta coefficient, which plays an important rôle in systematic risk management. This setting, which is based on the existence of trends for financial time series via nonstandard analysis (Fliess M., Join C.: A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory: Modelling, Analysis and Control, Fes, 2009, online: http://hal.inria.fr/inria-00352834/en/) leads to convincing computer experiments which are easily implementable.

Systematic risk analysis: first steps towards a new definition of beta

Michel FLIESS * , * * Cédric JOIN * , * * *

INTRODUCTION

This communication studies systematic (or market) risks in quantitative finance via the well known β coefficient (see, e.g., [3; 25]), which plays a key rôle since the introduction of the capital asset pricing model (CAPM) for portfolio management by Sharpe [START_REF] Sharpe | Capital asset prices: a theory of market equilibrium under conditions of risk[END_REF] and others. The following definition in plain words of β is borrowed from Lhabitant [START_REF] Lhabitant | Hedge Funds: Quantitative Insights[END_REF]: "Another interesting risk measure is called beta and is denoted by the Greek symbol β. Simply stated, β measures how risky a fund may be as compared to the overall stock market, typically approximated to the Standard and Poor's 500 or the MSCI World. A fund that moves in harmony with the market is said to have a beta of 1.0. Other things being equal, if the market goes up 10%, the fund is expected to go up up 10%. If the market goes down 10%, the fund is expected to go down up 10%. When a fund has a beta that is less than 1, it is supposed to move less in price than the market in general. Conversely, a fund with a beta higher than 1 is supposed to move more in price than the market in general. Hence, beta measures the risk of a fund by telling us how much its market price changes compared to changes in the overall stock market. A fund with a beta of more than 1 tends be riskier than the market, while a fund with a beta of less than 1 is less risky." The utilization of β for the purpose of β neutrality and hedging has become important for investments, including the hedge fund industry (see, e.g., [17; 18; 19; 20] and the references therein).

The linear character of the β coefficient explains some of its shortcomings (see, e.g., [7; 8] and the references therein).

The advantage of taking a time-varying β does not seem to have been yet clearly demonstrated (see, e.g., [1; 4]). A similar conclusion holds for the replacement of linear statistical regressions by nonlinear ones (see, e.g., [18; 20] and the references therein). We follow here another route, which is based on recent advances in automatic control and in signal processing:

(1) The Cartier-Perrin theorem [START_REF] Cartier | Integration over finite sets[END_REF] may be understood as a nonstandard definition of noises [START_REF] Fliess | Analyse non standard du bruit[END_REF]. It yields noise attenuation [START_REF] Fliess | Analyse non standard du bruit[END_REF] without having recourse to any probabilistic and statistical tool. It implies, therefore, the existence of trends in financial time series [START_REF] Fliess | A mathematical proof of the existence of trends in financial time series[END_REF].

A crucial assumption in technical analysis, which is quite popular among many traders (see, e.g., [2; 23] and [3; 19]), is thus confirmed. (2) A powerful derivatives estimation of noisy signals has been achieved by real-time techniques which are of algebraic flavor [15; 22]. Applications to new indicators in technical analysis have already been explored in [11; 13]. (3) New techniques for model-free control [10; 12] have been successfully applied in several concrete situations (automotive industry, smart material actuators, hydroelectric power plants, . . . ). 1 It shows that the necessity of writing down a "precise" mathematical model for controlling "complex" systems has been largely exaggerated in today's academic literature.

Our paper is organized as follows. Section 2 after a brief review of the existence of trends via the Cartier-Perrin theorem introduces a model-free definition of β and α. Section 3 sketches the techniques which are necessary for estimating beta and some related quantities. The numerous computer experiments in Section 4 show reliable forecasts of beta and of its abrupt changes via our techniques, which are easily implementable.

Analogous model-free methods will be developed in a near future in order to present various hedging strategies (see, e.g., [3; 25]), and a large range of highly efficient tools for risk analysis, which again will be easily implementable.

Value Reference value

T i m e Consider a time series f (t). The Cartier-Perrin theorem [START_REF] Cartier | Integration over finite sets[END_REF], which is expressed in the language of nonstandard analysis, 2 states that, under a very mild integrability condition, there exists the following additive decomposition of f (t): Those quick fluctuations are analogous to corrupting additive noises in engineering, and especially in signal processing and in automatic control. The practical calculation of the trend in [11; 13] is therefore achieved via estimation techniques which were developed elsewhere and for other purposes [15; 22]. Those techniques may be viewed as an extension of the familiar moving average methods which are central in technical analysis (see, e.g., [2; 23]).

f (t) = f trend (t) + f fluctuation (t) (1

Beta

Take two time series X(t) and Y (t), which satisfy the integrability assumptions of the Cartier-Perrin theorem. Assume moreover that the corresponding trends X trend (t) and Y trend (t) are locally smooth enough, i.e., they are locally C k for k large enough. Consider the space curve t, X trend (t), Y trend (t) in the Euclidean space with coordinates t, x, y. Its projection on the x, y plane is the plane curve C defined by

x C (t) = X trend (t), y C (t) = Y trend (t)
2 See, e.g., [START_REF] Fliess | A mathematical proof of the existence of trends in financial time series[END_REF] and especially [START_REF] Lobry | Nonstandard analysis and representation of reality[END_REF] for a general presentation and related references.

The tangent of C at a regular point, which is defined by

dX trend (t) dt , dY trend (t) dt , yields, if dX trend (t) dt = 0, ∆y C ≈ β(t)∆x C
(2) where

• ∆x C = x C (t + h) -x C (t), ∆y C = y C (t + h) -y C (t); • h ∈ R is "small"; • β(t) = dY trend dt dX trend dt (3)
When y C (t) may be viewed locally as a smooth function of x C (t), Equation (3) becomes

β(t) = dy C dx C (4)
With the original time series X(t), Y (t), Equation [START_REF] Franzoni | Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM[END_REF] shows that Equation ( 2) becomes ∆Y ≈ β(t)∆X + F ∆ (t) (5) where 2) and ( 5) relate obviously the amplitudes of the variations of X and Y during a "short" time interval. Let us emphasize that our definition of β is completely independent of any a priori mathematical modeling3 since some mild smoothness assumptions are the only claims on the data. Remark 2.2. Our β is, to the best of our knowledge, the first quantitative attempt to approach intermarket technical analysis (see, e.g., [2; 23]). Remark 2.3. The behavior of β(t) will also be related to higher order derivatives (see Section 4).

• ∆X = X(t + h) -X(t), ∆Y = Y (t + h) -X(t); • F ∆ (t)

Alpha

When Equation (4) holds, it yields the following straightforward definition of the α coefficient:

y C = α xI + x C xI βdx (6)
We recover a CAPM-like formula for α and β (see, e.g., [3; 25]), i.e., y

C = α 0 + βx C , if • β is constant, i.e.
, C is a straight line;

• x I = 0. Remark 2.4. A more intrinsic definition may be deduced from the geometry of the curve C: α xI is the ordinate of point of abscissa x = x I . Note however that this point may not be unique.

ESTIMATION TECHNIQUES

An integral formula for derivative estimation

Consider again the time series f (t) of Section 2.1. When f trend (t) in Equation ( 1) is smooth enough, elementary algebraic manipulations (see, e.g., [START_REF] Mboup | Numerical differentiation with annihilators in noisy environment[END_REF] and [START_REF] Collado | Analyse fréquentielle des dérivateurs algébriques[END_REF]) show that, for i ≥ 0,

d i f trend dt i (t) ≈ t t-T P i (τ )f (τ )dτ (7)
where

• T > 0 is the evaluation time window, which is usually quite "short"; • P is a polynomial function;

• when i = 0 (resp. i 0) we obtain an estimation of the trend (resp. of the derivative of order i of the trend). Remark 3.1. This setting, which is extremely useful in automatic control and signal processing (see, e.g., [15; 16] and the references therein), has already been employed in financial engineering for questions related to technical analysis [11; 13].

Estimating beta

The division in Equation ( 3) leads to numerical instabilities when applying Formula (7). The numerical implementation of the integral Formula (7) for estimating β via Equation ( 4) does therefore necessitate some care when compared to what has already been achieved in the past (see [START_REF] Mboup | Numerical differentiation with annihilators in noisy environment[END_REF] and [START_REF] Collado | Analyse fréquentielle des dérivateurs algébriques[END_REF]):

• the function x C (t) does not necessarily increase or decrease in a monotonic way, • the sampling with respect to x C is not necessarily uniform.

Practical details will be provided elsewhere. Remark 3.2. Similar calculations may be derived for estimating higher order derivatives like in Remark 2.3.

COMPUTER EXPERIMENTS

We compare with the S&P 500 index three quite different assets:

( Figure 2 displays the prices, which are normalized for an obvious comparison's sake, and their corresponding trends which are obtained as in [11; 13].

Consider, as in Section 2.2, the projections in the x, y plane. We exhibit the values of β and dβ dx , and their 20 days forecasts, which are obtained via similar extrapolation techniques to those in [11; 13]. The various Figures show that the predictions are quite good.

An abrupt change of β is a "sudden" and "important" variation of its values. Thanks to techniques in [13; 14], Figure 3-(d) displays the 20 days forecasts of abrupt changes of β for JPM. The results are again quite impressive. Remark 4.1. It is noteworthy that β is "almost" constant during large time intervals. According to Section 2.3, our simulations do confirm to some extent the well known CAPM modeling (see, e.g., [3; 25]). This traditional setting nevertheless is unable to deal with abrupt changes.

Figure 1 .

 1 Figure 1. 2D-3D illustration 2. A MODEL-FREE DEFINITION OF BETA 2.1 Quick fluctuations and trends in financial time series

  ) where f trend (t) and f fluctuation (t) are respectively the trend and a quickly fluctuating function around 0: • the quickly fluctuating function f fluctuation (t) is defined by the fact that its integral b a f fluctuation (τ )dτ over any finite interval [a, b] is infinitesimal, i.e., "very small"; • the trend f trend (t) is integrable and almost everywhere continuous.

  is a quickly fluctuating function[START_REF] Cartier | Integration over finite sets[END_REF]. Remark 2.1. Equations (

  JPMORGAN CHASE (JPM) from 1983-12-30 until 2009-07-21 (6267 days), (2) IBM from 1962-01-02 to 2009-07-21 (11776 days), (3) COCA COLA (CCE) from 1986-11-24 until 2009-07-21 (5519 days).

Remark 4 . 2 .Figure 2 Figure 3 Figure 4 .Figure 5 Figure 6 .

 4223456 Figure 2. Different assets

See some references in[10; 12]. All references to applications of model-free control, to which the authors are associated, are or will be soon available at http://hal.inria.fr.

Probabilistic and statistical modeling belong of course to mathematical modeling (see, e.g., the enlightening discussion in[START_REF] Bouleau | Mathématiques et risques financiers[END_REF]). Let us emphasize once again that we do not need any probabilistic and/or statistical framework for taking into account the uncertainty in finance (see the comments in[11; 13]).


domain: info scco shs

text:
Joseph Renner

Pascal Denis

Rémi Gilleron

WordNet Is All You Need: A Surprisingly Effective Unsupervised Method for Graded Lexical Entailment

We propose a simple unsupervised approach which exclusively relies on WordNet (Miller, 1995) for predicting graded lexical entailment (GLE) in English. Inspired by the seminal work of Resnik (1995), our method models GLE as the sum of two information-theoretic scores: a symmetric semantic similarity score and an asymmetric specicity loss score, both exploiting the hierarchical synset structure of Word-Net. Our approach also includes a simple disambiguation mechanism to handle polysemy in a given word pair. Despite its simplicity, our method achieves performance above the state of the art (Spearman ρ = 0.75) on HyperLex (Vulic et al., 2017), the largest GLE dataset, outperforming all previous methods, including specialized word embeddings approaches that use WordNet as weak supervision.

Introduction

A crucial aspect of language understanding is the ability to draw inferences between sentences. In many cases, these inferences are directly licensed by the semantics of words: e.g., the sentence a duck is in the room entails an animal is in the room simply because the concept of duck entails that of animal. These cases of (taxonomic) Lexical Entailment (LE) hold for words whose extensional denotations form a partial order: that is, the set of ducks is included in the set of birds which is itself included in the set of animals.

The taxonomic structure of lexical concepts is a dening aspect of human semantic memory and has been extensively studied in cognitive science as well as in NLP due to its multiple related applications. Initial research milestones include the construction of the WordNet lexical database (Beckwith et al., 2021;Miller, 1995), and the rst distributional approaches for automatically detecting hypernym-hyponym pairs (Hearst (1992); Snow et al. (2004); Baroni et al. (2012) (1975) and Kamp andPartee (1995), Vulic et al. (2017) have challenged the traditional view that LE is a binary relation, showing that it is instead a graded relation, based on human judgements (i.e., X entails Y to a certain degree). The concomitant release of Hyperlex,1 a data set of English word pairs scored by humans for the LE relation, has spurred new research into developing models for predicting Graded Lexical Entailment (GLE). A small subset of the dataset is presented in Table 1.

An intriguing research question is whether existing hand-crafted lexical hierarchies like WordNet are indeed able to capture GLE. Preliminary experiments by Vulic et al. (2017); Vulic and Mrksic (2017) report largely negative results: their best WordNet-only based system achieves a mere 0.234 Spearman correlation score with human judgments from Hyperlex. These poor performance results are blamed on the binary coding of the hypernymhyponym relation in WordNet. Yet LEAR (Vulic and Mrksic, 2017), the best GLE system to date achieving a 0.682 Spearman correlation score, uses WordNet as a source of constraints for specializing static word embedding models to the task. As static word embeddings alone achieve poor performance for GLE, the question of the contribution of Word-Net in the LEAR improved performance remains open.

In this paper, we propose a simple method that directly and solely exploits the internal structure of WordNet to predict GLE. Our approach relies on Information Content (IC), a continuous informationtheoretic measure introduced in Resnik (1995) to model semantic similarity in WordNet. Specically, we propose to model GLE as a trade-off between a symmetric semantic similarity score and an asymmetric specicity loss score, both of which are dened in terms of IC. Our method is completed with a disambiguation mechanism to address the fact that (G)LE is sense, rather than word specic, and is therefore sensitive to polysemy, an issue that has been largely overlooked in previous work: e.g., the noun plant entails building only in its working plant sense, and not in its botanical sense. This simple method achieves a 0.744 Spearman correlation score with human judgements, outperforming all previous systems, including specialized word embeddings methods and supervised models, as well as systems based on contextual language models.

To sum-up, our main contributions are threefold. First, we show that the internal structure of Word-Net, as revealed by information-theoretic measures and completed by a disambiguation mechanism, is a reliable predictor of the graded nature of LE. Second, our simple WordNet-only based approach provides a new state-of-the-art for GLE, outperforming previous methods that specialize word embeddings to the LE task using WordNet as weak supervision. Third, we provide a detailed analysis of our method showing the role of the two information-theoretic terms and the importance of sense disambiguation. We also present a simplied version of our scoring function without any frequency information in the computation of IC, which further improves the correlation score (0.753), thus emphasizing the singular importance of Wordnet hierarchical structure for GLE.

Proposed Method

Given a(n) (ordered) pair of words (X, Y ), instantiating a pair of latent (i.e., unknown) concepts (s X , s Y ), we aim to predict a score gle(X, Y ) indicating to what degree s X entails s Y . Specically, we propose to compute the score gle(X, Y ) as the sum of two terms: gle(X, Y ) = Sim(ŝ X , ŝY ) + SpecLoss(ŝ X , ŝY )

(1) where ŝX and ŝY are estimations of the latent concepts s X and s Y . The rst term Sim(ŝ X , ŝY ) stands for a (symmetric) semantic similarity function, capturing the fact that LE requires concepts to be semantically close. The second (asymmetric) term SpecLoss(ŝ X , ŝY ) encodes another important aspect of LE, namely the fact that there is generally a loss of specicity incurred by using ŝX (e.g., dog) instead of ŝY (e.g., animal), as the set denotation of ŝX is included in that of ŝY . 2While the general idea of modeling GLE as a trade-off between a similarity term and a speci-city term is already present in Vulic et al. ( 2017), the originality of our approach is to exclusively dene these terms using the hierarchical structure of WordNet, a lexical semantic graph made up of word senses (aka synsets) and relations between these synsets. This structure is accessed through information-theoretic measures that we dene now.

Information Content (IC) At rst glance, Word-Net might appear inadequate to model GLE because it encodes the hypernym-hyponym relation as a binary relation. But this claim is oblivious of two main facts. First, WordNet has some built-in gradedness as it models the hypernym-hyponym relation as a transitive relation. Second, the binary nature of the taxonomic links in WordNet can be easily bypassed by resorting to the notion of IC. This information-theoretical notion provides a continuous value for synsets by fully exploiting the tree structure associated with the hypernym-hyponym relation. Following Shannon (1951), Resnik (1995) proposes to quantify the information content (aka self-entropy) of each lexical concept s as the log of its inverse probability by IC(s) = log(1/P (s)). While one can simply estimate P (s) via the word frequencies associated with s in a large text corpus, the crucial innovation of Resnik (1995) was to use the taxonomic tree structure of WordNet in this estimation. Specically, P (s) is estimated as

P (s) =  h∈Hypo(s) wc(h)  k wc(k) (2)
where wc(s) is the word count for synset s in a large corpus 3 (in our case, Wikipedia), Hypo(s) denotes the set of all hyponym descendants of s (s included), and k stands over all synsets in Word-Net. By fully exploiting the hierarchical structure of WordNet, the notion of IC intuitively captures the monotonic relation between the generality (resp. specicity) of concepts, as measured by their height (resp. depth) in the taxonomy, and their informativeness.

Similarity We dene Sim as the IC-based similarity measure introduced in Lin (1998). The similarity between two synsets ŝX and ŝY is dened as the ratio between the information shared by the two concepts, modeled by the IC value of their least common subsumer node (denoted as lcs below), and the information needed to fully describe the two concepts, modeled as the sum of their ICs, leading to

Sim(ŝ X , ŝY ) = 2 IC(lcs(ŝ X , ŝY )) IC(ŝ X ) + IC(ŝ Y ) . (3)

Specicity Loss

The above similarity measure is arguably a poor predictor of GLE if used alone. This measure will assign high scores to co-hyponyms (e.g., cat and dog) and equal scores to the same hypernym-hyponym pair whatever the order. We therefore need to complement this measure with another, asymmetric measure that is able to quantify the fact that the entailed concept is typically less informative. For this, we dene the specity loss by

SpecLoss(ŝ X , ŝY ) = 1 - IC(ŝ Y ) IC(ŝ X ) . ( 4
)
This function returns values closer to 1.0 when the ŝX is more specic than ŝY and lower (possibly negative) values when ŝY is more specic than ŝX . The example of co-hyponyms shows the importance of the trade-off between the two scores. Indeed, while the similarity is maximized, the speci-city loss is minimized as both synsets have similar 3 wc(s) is the occurrence count of all words associated with s in WordNet, where a word count is normalized by its total number of synsets.

IC values, resulting in a sum that indicates relatively low GLE strength. Similarly, when ŝX is a hypernym of ŝY , the similarity score will be high but the specicity score will be low (even negative) and reduce the sum to a more appropriate score.

Synset Disambiguation Turning to the issue of estimating the latent synsets s X and s Y , we propose to jointly select a pair of synsets with ŝX , ŝY = argmax

s X ∈S(X),s Y ∈S(Y ) Sim(s X , s Y ) (5)
where S(X) and S(Y ) denote the set of possible synsets for X and Y , respectively. That is, we select the pair of synsets with the maximum similarity value. For example, given the words plant and building, this method should hopefully select the synset corresponding to plant as a working plant, not the synset corresponding its botanical sense. We hypothesize that humans implicitly perform such joint sense selection when asked to score the relation between plant and building.

Experiments

This section presents our experimental framework and results of our approach against various baselines and competing systems.4

Dataset and Settings

Our evaluation dataset is the Hyperlex dataset (Vulic et al., 2017), which contains 2616 English word pairs (2163 noun pairs and 453 verb pairs). Extracted from WordNet, the pairs from the dataset were scored on a 0-6 scale by human subjects based on the prompt "To what degree is X a type of Y ?".5 Scores of the different systems are compared using Spearman's ρ correlation (Spearman, 1904). As our method is fully unsupervised, we can evaluate it and other competing unsupervised methods and baselines on the entire Hyperlex dataset.

For ensuring fair comparison with supervised competitors, we also report the performance of our method on specic test subsets of Hyperlex. Specifically, we rely on the two test subsets provided by the Hyperlex authors: a random subset (25% of the pairs) and a train/validation/test split without any lexical overlap (see Vulic et al. (2017) for more details). Finally, note that we use a text dump of Wikipedia for counting word occurrences for IC calculation and frequency baselines.

Unsupervised Systems

Static Word Embeddings and WordNet Baselines Our baseline systems are taken or inspired from Vulic et al. (2017). These include a cosine similarity function based on Word2Vec (Mikolov et al., 2013) and the best WordNet-only method reported in Vulic et al. (2017), using the Wu-Palmer similarity (Wu and Palmer, 1994). Finally, Vulic et al. (2017) introduce a strong baseline (ρ score of 0.279) that combines a specicity term, dened in terms of a concept frequency ratio (i.e., 1 -wc(X) wc(Y ) for a word pair (X, Y )), and a Word2Vec cosine similarity term acting as a threshold. 6 We propose a variation of this approach, by instead summing the Word2Vec vector cosine similarity and the concept frequency ratio. Recall that static embeddings collapse all word senses, thus prevent the use of disambiguation technique in these methods.

CLM-based Methods

The success of contextual language models (CLM) on many tasks led us to study their usage for GLE. We tested several techniques of deriving static representations from contextual representations following Apidianaki (2023). We found that the best performing one was the method introduced by Misra et al. (2021) (called taxonomic verication) for the related task of graded typicality; in this case, the method uses a GPT-2-XL (Radford et al., 2019) pretrained model. 7 In this approach, taxonomic sentences of the form "A(n) X is a(n) Y" are scored by the model, calculating P (Y |A(n) X is a(n)). Notice that such contextual prompts allow for some implicit joint disambiguation of the two words.

Specialized Static Embeddings

The last competitor is the current state-of-the-art LEAR system (Vulic and Mrksic, 2017), which is based on static embeddings specialized for LE through WordNet-derived constraints. Other systems which also use WordNet constraints are Hyper-Vec (Nguyen et al., 2017) and Poincaré Embeddings (Nickel and Kiela, 2017) but their reported performance is lower on the GLE task.

Comparing Unsupervised Methods As shown in Table 2, all three baseline systems from Vulic et al. ( 2017) achieve a ρ score below 0.3. Our baseline combining the concept frequency ratio and a Word2Vec cosine similarity achieves a 0.314 ρ score. Our CLM-based method achieves a 0.425 ρ score which is the best score achieved so far on the GLE task using CLMs. The best competitor to date is the LEAR system with a 0.686 ρ score (taken from Vulic and Mrksic ( 2017)). 8Our WordNet-based method, denoted by WordNet-SSD, reaches a 0.744 ρ score. To our knowledge, this is the best correlation score reported so far on Hyperlex. And it is indeed quite close to the human inter-annotator agreement correlation score of 0.854, which we can take as an upper bound on this task. These results strongly suggest that the hierarchical structure of WordNet provide enough information to accurately model graded LE, and that previous WordNet-based approaches have so far failed at properly leveraging this information.

Supervised Baselines and Competing Systems

We also compare our method's performance to that of the supervised approach presented in Vulic et al. (2017). This method trains a supervised linear regression model on Word2Vec embeddings. As another baseline, we also train a supervised linear regression model using BERT token embeddings, instead of Word2Vec embeddings. Results on the two test splits of Hyperlex are presented in Table 3. The regression model with static embeddings achieves a Spearman's ρ of 0.53 and 0.45 for the random and lexical test splits, respectively, and of 0.420 and 0.257 when using BERT embeddings. On the same splits, our unsupervised method signicantly outperforms these supervised models, reaching ρ scores of 0.605 and 0.636, respectively.

Analysis

This section analyses the different components of our approach via several targeted ablation studies.

  ; Dagan et al.

	X	Y	LE Score
	duck	animal	5.92
	duck	bird	5.75
	conict	disagreement	5.20
	competence ability	4.64
	aura	light	3.69
	sofa	chair	3.38
	butter	cream	2.69
	noun	adjective	0.50
	rhyme	dinner	0.00

Table

1

: The human lexical entailment scores (0-6) for a small subset of the Hyperlex

(Vulic et al., 2017)

dataset. Each row should be read as: X entails Y to a degree of LE score.

(2013) inter alia). More recently, an important strand of research has led to the development of word representation models that are able to geometrically express asymmetric relations like LE in the embedding space

(Roller and Erk, 2016; Vilnis and McCallum, 2015; Nickel and Kiela, 2017)

.

Inspired by the pioneering works of Rosch

https://github.com/cambridgeltl/hyperlex

Synonyms are an obvious exception, as they trivially entail each other while having the same denotation hence speci-city.

Our code and data are publicly available at: https:// gitlab.inria.fr/magnet/GLE_emnlp.

It is important to note the use of WordNet in creating Hyperlex was restricted to word pair selection, so no structural information from WordNet has inuenced the human scores.

See Equation (13) inVulic et al. (2017).

The other pretrained models we tested were bert-base and bert-large(Devlin et al., 2018), roberta-large(Liu et al., 2019), deberta-v3-large (He et al., 2021), and pythia-1b (Biderman et al., 2023).

Note that the system in Wang et al. (2020) is based on the LEAR system, but evaluated the SemEval 2020 English task 2, which is a different (fourth) subset of Hyperlex, achieving a Spearman's ρ of 0.696. We evaluated our method on this subset as well, achieving a Spearman's rho of 0.741.