Papers by Romaric Besançon

3 papers
Few-Shot Event Argument Extraction Based on a Meta-Learning Approach (2024.naacl-srw)

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Challenge: Recent studies on few-shot event extraction focus on event trigger detection and argument extraction in meta-learning contexts.
Approach: They propose to use prototypical networks to perform few-shot event argument extraction . they propose to inject syntactic knowledge into the model to enhance relation embeddings .
Outcome: The proposed approach achieves strong performance on ACE 2005 in several few-shot configurations.
Building a Multimodal Entity Linking Dataset From Tweets (2020.lrec-1)

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Challenge: Entity linking is a task that aims at associating an entity mention with a unique entity in a knowledge base.
Approach: They propose a method to quasi-automatically build annotated datasets to evaluate methods on the Entity Linking task.
Outcome: The proposed method builds annotated datasets of tweets with ambiguous mentions and a Twitter KB defining the entities.
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop? (2022.coling-1)

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Challenge: Recent developments have shown that pre-trained language models are effective soft reasoners over language.
Approach: They propose to model multi-hop reasoning process as a sequence of explicit single-hop steps.
Outcome: The proposed model improves on multiple-choice question answering and reading comprehension with 68.4% and 16.0% w.r.t. classic PLMs.

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