Reinforcement Learning for Transition-Based Mention Detection

03/13/2017
by   Georgiana Dinu, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/08/2023

Reinforcement Learning for Topic Models

We apply reinforcement learning techniques to topic modeling by replacin...
research
04/26/2016

Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning

As a genetics-based machine learning technique, zeroth-level classifier ...
research
05/09/2017

Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning

We present a new deep meta reinforcement learner, which we call Deep Epi...
research
07/10/2019

Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

In this paper, we present a Bayesian view on model-based reinforcement l...
research
08/13/2020

Reinforcement Learning with Trajectory Feedback

The computational model of reinforcement learning is based upon the abil...
research
06/22/2023

Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping

Reinforcement learning often needs to deal with the exponential growth o...

Please sign up or login with your details

Forgot password? Click here to reset