Research on State Modeling of Multiple Parsers Based on Attention Mechanism

Abstract

Transition-based dependency parsing is a fast and effective approach for dependency parsing. When the sentence is input to the transition-based dependency parser, the parser predicts a series of parsing actions from left to right. Stack information, which plays an essential role in the parsing process. In this paper, we propose a dependency parser that is based on bidirectional-LSTMs and an approach that uses the attention mechanism for learning representations of parser states. This model simulates the global state of the parser by capturing more relevant information, we dig into the stack information, buffer information, the word information that has been parsed out of the stack, the complete historical information of the actions taken by the parser, and semantic information during the parsing process. The words popped from the stack at each time step and the predicted action are then applied to the parsing task at the next time step, which is helpful to the parsing performance of the parser.