End-to-End Chinese Parsing Exploiting Lexicons
This work addresses the problem of improving Chinese parsing accuracy for NLP researchers by integrating traditional pipeline steps into a single end-to-end model.
This paper proposes an end-to-end Chinese parsing model that takes character inputs and jointly learns word segmentation, part-of-speech tags, and dependency structures. The model achieves state-of-the-art results on three Chinese parsing benchmark datasets.
Chinese parsing has traditionally been solved by three pipeline systems including word-segmentation, part-of-speech tagging and dependency parsing modules. In this paper, we propose an end-to-end Chinese parsing model based on character inputs which jointly learns to output word segmentation, part-of-speech tags and dependency structures. In particular, our parsing model relies on word-char graph attention networks, which can enrich the character inputs with external word knowledge. Experiments on three Chinese parsing benchmark datasets show the effectiveness of our models, achieving the state-of-the-art results on end-to-end Chinese parsing.