Huan Wang


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Unsupervised Paraphrasing with Pretrained Language Models
Tong Niu | Semih Yavuz | Yingbo Zhou | Nitish Shirish Keskar | Huan Wang | Caiming Xiong
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures. However, previous explorations have largely focused on supervised methods, which require a large amount of labeled data that is costly to collect. To address this drawback, we adopt a transfer learning approach and propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking (DB). To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair (QQP) and the ParaNMT datasets and is robust to domain shift between the two datasets of distinct distributions. We also demonstrate that our model transfers to paraphrasing in other languages without any additional finetuning.

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BatchMixup: Improving Training by Interpolating Hidden States of the Entire Mini-batch
Wenpeng Yin | Huan Wang | Jin Qu | Caiming Xiong
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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A Constituent Syntactic Parse Tree Based Discourse Parser
Zhongyi Li | Hai Zhao | Chenxi Pang | Lili Wang | Huan Wang
Proceedings of the CoNLL-16 shared task


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PCFG Parsing for Restricted Classical Chinese Texts
Liang Huang | Yinan Peng | Huan Wang | Zhenyu Wu
COLING-02: The First SIGHAN Workshop on Chinese Language Processing