Xiaoyan Li


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Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations
Xiaoyan Li | Sun Sun | Yunli Wang
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learning to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.


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Exploiting Multi-Features to Detect Hedges and their Scope in Biomedical Texts
Huiwei Zhou | Xiaoyan Li | Degen Huang | Zezhong Li | Yuansheng Yang
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task


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Evaluating Question-Answering Techniques in Chinese
Xiaoyan Li | W. Bruce Croft
Proceedings of the First International Conference on Human Language Technology Research