Fiche de TAILLE Bruno

Titre : Contextualization and Generalization in Entity and Relation Extraction
Date : 11 mars 2022
Heure: 09:00
Résumé : In this thesis, we study the behaviour of state-of-the-art models regarding generalization to facts unseen during training in two important Information Extraction tasks: Named Entity Recognition and Relation Extraction. Indeed, traditional benchmarks present important lexical overlap between mentions and relations used for training and evaluating models, whereas the main interest of Information Extraction is to extract previously unknown information. We propose empirical studies to separate performance based on mention and relation overlap with the training set and find that pretrained Language Models are mainly beneficial to detect unseen mentions, in particular out-of-domain. While this makes them suited for real use cases, there is still a gap in performance between seen and unseen that hurt generalization to new facts. In particular, even state-of-the-art ERE models rely on a shallow retention heuristic, basing their prediction more on arguments surface forms than context.
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