Reinforcement Learning for Transition-Based Mention Detection
This work addresses mention detection in NLP, presenting an incremental improvement with a novel method for a known bottleneck.
The paper tackles mention detection by introducing a reinforcement learning approach with an action-based formulation that allows flexible revision of past labeling decisions, achieving results comparable to a supervised counterpart while offering greater flexibility in handling longer mentions.
This paper describes an application of reinforcement learning to the mention detection task. We define a novel action-based formulation for the mention detection task, in which a model can flexibly revise past labeling decisions by grouping together tokens and assigning partial mention labels. We devise a method to create mention-level episodes and we train a model by rewarding correctly labeled complete mentions, irrespective of the inner structure created. The model yields results which are on par with a competitive supervised counterpart while being more flexible in terms of achieving targeted behavior through reward modeling and generating internal mention structure, especially on longer mentions.