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Zhixing Tan

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4 papers

1 author row

NeurIPS Conference 2022 Conference Paper

Molecule Generation by Principal Subgraph Mining and Assembling

Xiangzhe Kong
Wenbing Huang
Zhixing Tan
Yang Liu

Molecule generation is central to a variety of applications. Current attention has been paid to approaching the generation task as subgraph prediction and assembling. Nevertheless, these methods usually rely on hand-crafted or external subgraph construction, and the subgraph assembling depends solely on local arrangement. In this paper, we define a novel notion, principal subgraph that is closely related to the informative pattern within molecules. Interestingly, our proposed merge-and-update subgraph extraction method can automatically discover frequent principal subgraphs from the dataset, while previous methods are incapable of. Moreover, we develop a two-step subgraph assembling strategy, which first predicts a set of subgraphs in a sequence-wise manner and then assembles all generated subgraphs globally as the final output molecule. Built upon graph variational auto-encoder, our model is demonstrated to be effective in terms of several evaluation metrics and efficiency, compared with state-of-the-art methods on distribution learning and (constrained) property optimization tasks.

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IJCAI Conference 2020 Conference Paper

Modeling Voting for System Combination in Machine Translation

Xuancheng Huang
Jiacheng Zhang
Zhixing Tan
Derek F. Wong
Huanbo Luan
Jingfang Xu
Maosong Sun
Yang Liu

System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance. Although early statistical approaches to system combination have been proven effective in analyzing the consensus between hypotheses, they suffer from the error propagation problem due to the use of pipelines. While this problem has been alleviated by end-to-end training of multi-source sequence-to-sequence models recently, these neural models do not explicitly analyze the relations between hypotheses and fail to capture their agreement because the attention to a word in a hypothesis is calculated independently, ignoring the fact that the word might occur in multiple hypotheses. In this work, we propose an approach to modeling voting for system combination in machine translation. The basic idea is to enable words in hypotheses from different systems to vote on words that are representative and should get involved in the generation process. This can be done by quantifying the influence of each voter and its preference for each candidate. Our approach combines the advantages of statistical and neural methods since it can not only analyze the relations between hypotheses but also allow for end-to-end training. Experiments show that our approach is capable of better taking advantage of the consensus between hypotheses and achieves significant improvements over state-of-the-art baselines on Chinese-English and English-German machine translation tasks.

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AAAI Conference 2018 Conference Paper

Deep Semantic Role Labeling With Self-Attention

Zhixing Tan
Mingxuan Wang
Jun Xie
Yidong Chen
Xiaodong Shi

Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. However, it remains a major challenge for RNNs to handle structural information and long range dependencies. In this paper, we present a simple and effective architecture for SRL which aims to address these problems. Our model is based on self-attention which can directly capture the relationships between two tokens regardless of their distance. Our single model achieves F1 = 83. 4 on the CoNLL-2005 shared task dataset and F1 = 82. 7 on the CoNLL-2012 shared task dataset, which outperforms the previous state-of-the-art results by 1. 8 and 1. 0 F1 score respectively. Besides, our model is computationally efﬁcient, and the parsing speed is 50K tokens per second on a single Titan X GPU.

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AAAI Conference 2017 Conference Paper

Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation

Jinsong Su
Zhixing Tan
Deyi Xiong
Rongrong Ji
Xiaodong Shi
Yang Liu

Neural machine translation (NMT) heavily relies on wordlevel modelling to learn semantic representations of input sentences. However, for languages without natural word delimiters (e. g. , Chinese) where input sentences have to be tokenized ﬁrst, conventional NMT is confronted with two issues: 1) it is difﬁcult to ﬁnd an optimal tokenization granularity for source sentence modelling, and 2) errors in 1-best tokenizations may propagate to the encoder of NMT. To handle these issues, we propose word-lattice based Recurrent Neural Network (RNN) encoders for NMT, which generalize the standard RNN to word lattice topology. The proposed encoders take as input a word lattice that compactly encodes multiple tokenizations, and learn to generate new hidden states from arbitrarily many inputs and hidden states in preceding time steps. As such, the word-lattice based encoders not only alleviate the negative impact of tokenization errors but also are more expressive and ﬂexible to embed input sentences. Experiment results on Chinese-English translation demonstrate the superiorities of the proposed encoders over the conventional encoder.

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