End-to-End Chinese Parsing Exploiting Lexicons

TL;DR

This paper introduces an end-to-end Chinese parsing model that jointly learns segmentation, POS tagging, and dependency parsing from characters, leveraging word-character graph attention networks to incorporate external word knowledge, achieving state-of-the-art results.

Contribution

It presents a novel end-to-end Chinese parsing approach using graph attention networks to integrate word knowledge directly from characters.

Findings

Achieved state-of-the-art results on three Chinese parsing benchmarks.

Demonstrated effectiveness of character-based joint learning.

Validated the model's superiority over traditional pipeline systems.

Abstract

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.