Minutes from the eleventh WG 13 meeting (online 2025-03-12 12:00 CET)

Agenda

Subtask reports: Task 3.2: Shared task on morphosyntactic parsing (Omer Goldman, Leonie Weissweiler, Reut Tsarfaty) Google group Training data and future evaluation code UniDive webpage Task 3.4: Evaluation campaign PARSEME 2.0 (Manon Scholivet, Agata Savary) Taks 3.5: Evaluation campaign AdMIRe (Thomas Pickard, Aline Villavicencio) General discussion

Next meeting: May 6, 12.00 CEST (on line) List of Participants Gülşen Eryiğit (chair) Joakim Nivre (co-chair) Roberto Antonio Díaz Hernández Ali Basirat Csilla Horváth Manon Scholivet Rob van der Goot Agata Savary Ranka Stanković Thomas Pickard Aline Villavicencio Tanja Samardzic Dawit J Alina Wróblewska Luka Terčon Olha Kanishcheva Dan Zeman Takuya Nakamura Federica Gamba Carlos Ramisch Flavio Massimiliano Cecchini Gosse Bouma Rusudan Makhachashvili Voula Giouli Ebru Çavuşoğlu Omer Goldman Reut Tsarfaty Chaya Liebeskind Faruk Mardan Adriana Pagano Ilan Kernerman Kutay acar Ludmila Malahov Teresa Lynn Lucía Amorós-Poveda PARSEME shared task (Manon, Agata) subtask 1 (PARSEME 2.0) quite established framework novelty: non-verbal MWEs, diversity measures subtask 2 (MWE generation) given a context with eliminated MWEs, restore this MWE Problems: how to evaluate the system [ALINE] Consider taking into account the level of difficulty of the items? For example, some items will be more ambiguous and more difficult to determine [JOAKIM] It is unclear which capacity of models we test [TOM] Very difficult to evaluate, even manually. subtask 3 (MWE comprehension/disambiguation) Given a sentence and a span of a potential idiomatic expressions, classify it as idiomatic, literal or coincidental [GULSEN] There are some datasets for this task. Maybe the 3rd category complicates the things. [JOAKIM] [TOM] The same as SemEval 2022 (EN, PT, Galician). There are artefact issues (the models don’t really pay attention to the context). subtask 4 (paraphrasing) Given a sentence, rephrase it so that there are no MWEs [AGATA] The input should be raw text, without a span. Objective: simplification of a text. [JOAKIM] The most natural tasks among (2, 3 and 4). Close to what people do with LLMs. Can we avoid doing manual evaluation? (LLM as judge) [TOM] His favorite [ALINE] They work with questionnaires for humans for this problem. There is a synonym dataset. Another task: collect sentences with synonyms of MWEs. [ALINE] Sometimes the simplest way to express a meaning is with a MWE. Questions: Which subtasks to choose? How to evaluate them?

AdMIRe extension Tom’s slideshttps://docs.google.com/presentation/d/1PLeZfHiZeU7NY8BS6AmnEunnsPsk_MOwucOSzYusBD8/edit?usp=sharing Task websitehttps://semeval2025-task1.github.io/ Data curation guidelines & notes