Lu Zhang


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MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
Lu Zhang | Mo Yu | Tian Gao | Yue Yu
Findings of the Association for Computational Linguistics: EMNLP 2020

Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.


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How Transferable are Neural Networks in NLP Applications?
Lili Mou | Zhao Meng | Rui Yan | Ge Li | Yan Xu | Lu Zhang | Zhi Jin
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Lili Mou | Yiping Song | Rui Yan | Ge Li | Lu Zhang | Zhi Jin
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a “sequence to backward and forward sequences” model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.

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Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Lili Mou | Rui Men | Ge Li | Yan Xu | Lu Zhang | Rui Yan | Zhi Jin
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Discriminative Neural Sentence Modeling by Tree-Based Convolution
Lili Mou | Hao Peng | Ge Li | Yan Xu | Lu Zhang | Zhi Jin
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


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Extracting Paraphrases of Technical Terms from Noisy Parallel Software Corpora
Xiaoyin Wang | David Lo | Jing Jiang | Lu Zhang | Hong Mei
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers