Papers by Mathieu Constant

5 papers
„Mann“ is to “Donna” as「国王」is to « Reine » Adapting the Analogy Task for Multilingual and Contextual Embeddings (2023.starsem-1)

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Challenge: a lack of comparable multilingual benchmarks and a consensual evaluation protocol for contextual models remains an open question.
Approach: They propose a multilingual analogy dataset and evaluate human and contextual embedding performance.
Outcome: The proposed dataset evaluates human and contextual embedding models on the analogy task.
Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs? (2024.lrec-main)

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Challenge: lexical resources are essential for the development of NLP systems, but with advances in language models and deep learning, they are increasingly being replaced by web-derived text.
Approach: They propose a resource-centric study of link prediction approaches over French lexical-semantic graphs.
Outcome: The proposed method is more accurate and reliable than previous methods.
Rigor Mortis: Annotating MWEs with a Gamified Platform (2020.lrec-1)

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Challenge: gamification of the platform should be improved, in order to attract and retain more players.
Approach: They propose to use a gamified crowdsourcing platform to evaluate the intuition of speakers and then train them to annotate multi-word expressions in French corpora.
Outcome: The proposed platform evaluates the speakers' intuition and trains them to annotate multi-word expressions in French corpora.
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces (2022.tacl-1)

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Challenge: Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm . a novel decomposition of Transformer output embeddables is demonstrated .
Approach: They propose to decompose Transformer output embeddings into a sum of vector factors . they show multi-head attentions and feed-forwards are not equally useful in downstream applications .
Outcome: The proposed method outperforms recurrent architectures on a wide variety of tasks.
Complex Word Identification: A Comparative Study between ChatGPT and a Dedicated Model for This Task (2024.lrec-main)

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Challenge: Existing methods to assess lexical complexity are used to evaluate the difficulty of vocabulary for language learners.
Approach: They propose to use pre-trained language models to assess the complexity of a word based on its context.
Outcome: The proposed method outperforms the best systems in SemEval-2021.

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