05a - Apprentissage par imitation pour l'étiquetage de séquences : vers une formalisation des méthodes d'étiquetage easy-first (taln2015)

Sessions orales TALN 2015 ? Mardi 23 juin 2015Session Extraction d'information Apprentissage par imitation pour l'étiquetage de séquences : vers une formalisation des méthodes d'étiquetage easy-first Elena Knyazeva,Guillaume Wisniewski et François Yvon Résumé : Structured learning techniques,aimed at modeling structured objects such as labeled trees or strings,are computationally expensive. Many attempts have been made to reduce their complexity,either to speed up learning et inference,or to take richer dependencies into account.  These attempts typically rely on approximate inference techniques and usually provide very little   theoretical guarantee regarding the optimality of the solutions they find.   In this work we study a new formulation of structured learning where inference is primarily viewed as an incremental process along which a solution is progressively computed. This framework generalizes several structured learning approaches. Building on the connections between this framework and reinforcement learning,we propose a theoretically sound method to learn to perform approximate inference. Experiments on four sequence labeling tasks show that our   approach is very competitive when compared to several strong baselines. Structured learning techniques,aimed at modeling structured objects such as labeled trees or strings,are computationally expensive. Many attempts have been made to reduce their complexity,either to speed up learning and inference,or to take richer dependencies into account.  These attempts typically rely on approximate inference techniques and usually provide very little theoretical guarantee regarding the optimality of the solutions they find.

Disciplines : Traduction automatique,Informatique appliquée à la linguistique
Mots-clés : traitement automatique du langage naturel,taln2015
Type de document : image en mouvement
Type de ressource : lecture
Niveau : higher education