A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
This addresses a bottleneck in Chinese NLP by improving joint segmentation and parsing, though it is incremental as it builds on existing joint models.
The authors tackled the problem of error propagation in Chinese dependency parsing due to separate word segmentation by proposing a graph-based joint model that integrates both tasks, achieving state-of-the-art results in segmentation and parsing and reducing the performance gap with gold-segmented models when combined with BERT.
Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined on word-level. Therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffer from error propagation and unable to directly make use of the character-level pre-trained language model (such as BERT). In this paper, we propose a graph-based model to integrate Chinese word segmentation and dependency parsing. Different from previous transition-based joint models, our proposed model is more concise, which results in fewer efforts of feature engineering. Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. Besides, when BERT is combined, our model can substantially reduce the performance gap of dependency parsing between joint models and gold-segmented word-based models. Our code is publicly available at https://github.com/fastnlp/JointCwsParser.