A Tree Search Algorithm for Sequence Labeling

by   Yadi Lao, et al.

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.


page 1

page 2

page 3

page 4


Sequence Tagging with Policy-Value Networks and Tree Search

In this paper we propose a novel reinforcement learning based model for ...

Semantic Tagging with LSTM-CRF

In the present paper, two models are presented namely LSTM-CRF and BERT-...

TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags

So far, discontinuous named entity recognition (NER) has received increa...

Tag Recommendation by Word-Level Tag Sequence Modeling

In this paper, we transform tag recommendation into a word-based text ge...

Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks

A Pseudo-Random Number Generator (PRNG) is any algorithm generating a se...

Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger

Researches on Indonesian named entity (NE) tagger have been conducted si...

Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning

We tackle tag-based query refinement as a mobile-friendly alternative to...

Please sign up or login with your details

Forgot password? Click here to reset