A Tree Search Algorithm for Sequence Labeling

TL;DR

This paper introduces MM-Tag, a novel reinforcement learning model using Monte Carlo tree search for sequence tagging, which outperforms existing methods like CRF and LSTM-based models on chunking tasks.

Contribution

The paper is the first to apply MCTS-enhanced MDP to sequence tagging, integrating reinforcement learning with LSTMs for improved accuracy.

Findings

MM-Tag outperforms state-of-the-art baselines.

MCTS improves exploration in sequence tagging.

Reinforcement learning effectively trains the model.

Abstract

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of the whole sentence are produced. The policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a better search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM.