Zhuang Liu


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Enhancing Chinese Word Segmentation via Pseudo Labels for Practicability
Kaiyu Huang | Junpeng Liu | Degen Huang | Deyi Xiong | Zhuang Liu | Jinsong Su
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation
Kaiyu Huang | Degen Huang | Zhuang Liu | Fengran Mo
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Word-level information is important in natural language processing (NLP), especially for the Chinese language due to its high linguistic complexity. Chinese word segmentation (CWS) is an essential task for Chinese downstream NLP tasks. Existing methods have already achieved a competitive performance for CWS on large-scale annotated corpora. However, the accuracy of the method will drop dramatically when it handles an unsegmented text with lots of out-of-vocabulary (OOV) words. In addition, there are many different segmentation criteria for addressing different requirements of downstream NLP tasks. Excessive amounts of models with saving different criteria will generate the explosive growth of the total parameters. To this end, we propose a joint multiple criteria model that shares all parameters to integrate different segmentation criteria into one model. Besides, we utilize a transfer learning method to improve the performance of OOV words. Our proposed method is evaluated by designing comprehensive experiments on multiple benchmark datasets (e.g., Bakeoff 2005, Bakeoff 2008 and SIGHAN 2010). Our method achieves the state-of-the-art performances on all datasets. Importantly, our method also shows a competitive practicability and generalization ability for the CWS task.


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DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering
Huiwei Zhou | Bizun Lei | Zhe Liu | Zhuang Liu
Proceedings of the 18th BioNLP Workshop and Shared Task

In medical domain, given a medical question, it is difficult to manually select the most relevant information from a large number of search results. BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining technology to automatically judge whether a search result is an answer to the medical question. The main challenge of QA task is how to mine the semantic relation between question and answer. We propose BioBERT Transformer model to tackle this challenge, which applies Transformers to extract semantic relation between different words in questions and answers. Furthermore, BioBERT is utilized to encode medical domain-specific contextualized word representations. Our method has reached the accuracy of 76.24% and spearman of 17.12% on the BioNLP 2019 QA task.


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Research on attention memory networks as a model for learning natural language inference
Zhuang Liu | Degen Huang | Jing Zhang | Kaiyu Huang
Proceedings of the Workshop on Structured Prediction for NLP