Miao Li


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A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features
Hongyin Tang | Miao Li | Beihong Jin
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Text generation is among the most fundamental tasks in natural language processing. In this paper, we propose a text generation model that learns semantics and structural features simultaneously. This model captures structural features by a sequential variational autoencoder component and leverages a topic modeling component based on Gaussian distribution to enhance the recognition of text semantics. To make the reconstructed text more coherent to the topics, the model further adapts the encoder of the topic modeling component for a discriminator. The results of experiments over several datasets demonstrate that our model outperforms several states of the art models in terms of text perplexity and topic coherence. Moreover, the latent representations learned by our model is superior to others in a text classification task. Finally, given the input texts, our model can generate meaningful texts which hold similar structures but under different topics.


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An combined sentiment classification system for SIGHAN-8
Qiuchi Li | Qiyu Zhi | Miao Li
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing


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Improving Bilingual Lexicon Extraction Performance from Comparable Corpora via Optimizing Translation Candidate Lists
Shaoqi Wang | Miao Li | Zede Zhu | Zhenxin Yang | Shizhuang Weng
Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing


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Building Comparable Corpora Based on Bilingual LDA Model
Zede Zhu | Miao Li | Lei Chen | Zhenxin Yang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)