Zizhuo Shen


pdf bib
Semantic-aware Chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser
Lanqiu Zhang | Zizhuo Shen | Yanqiu Shao
Proceedings of the 19th Chinese National Conference on Computational Linguistics

Deep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well integrated linguistic information into the deep learningbased Chinese zero pronoun resolution model.This paper adopts the idea based on the pre-trained model, and integrates the semantic representations in the pre-trained Chinese semantic dependency graph parser into the Chinese zero pronoun resolution model. The experimental results on OntoNotes-5.0 dataset show that our proposed Chinese zero pronoun resolution model with pretrained Chinese semantic dependency parser improves the F-score by 0.4% compared with our baseline model, and obtains better results than other deep learning-based Chinese zero pronoun resolution models. In addition, we integrate the BERT representations into our model so that the performance of our model was improved by 0.7% compared with our baseline model.

pdf bib
Combining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Shaolei Wang | Baoxin Wang | Jiefu Gong | Zhongyuan Wang | Xiao Hu | Xingyi Duan | Zizhuo Shen | Gang Yue | Ruiji Fu | Dayong Wu | Wanxiang Che | Shijin Wang | Guoping Hu | Ting Liu
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

Grammatical error diagnosis is an important task in natural language processing. This paper introduces our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED). CGED aims to diagnose four types of grammatical errors which are missing words (M), redundant words (R), bad word selection (S) and disordered words (W). Our system is built on the model of multi-layer bidirectional transformer encoder and ResNet is integrated into the encoder to improve the performance. We also explore two ensemble strategies including weighted averaging and stepwise ensemble selection from libraries of models to improve the performance of single model. In official evaluation, our system obtains the highest F1 scores at identification level and position level. We also recommend error corrections for specific error types and achieve the second highest F1 score at correction level.