# Reinforcement Learning for Transition-Based Mention Detection

## TL;DR

This paper introduces a reinforcement learning approach to mention detection, allowing flexible token grouping and mention labeling, achieving competitive results while enabling targeted behavior and internal structure modeling.

## Contribution

It presents a novel reinforcement learning formulation for mention detection that allows dynamic revision of labels and internal mention structure modeling.

## Key findings

- Achieves results comparable to supervised methods.
- More flexible in handling longer mentions.
- Enables targeted behavior through reward modeling.

## Abstract

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.