Bo Zhang


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Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks
Bo Zhang | Yue Zhang | Rui Wang | Zhenghua Li | Min Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer “who expressed what kind of sentiment towards what?”. Due to the scarcity of labeled data, ORL remains challenging for data-driven methods. In this work, we try to enhance neural ORL models with syntactic knowledge by comparing and integrating different representations. We also propose dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels. In order to compensate for parser inaccuracy and reduce error propagation, we introduce multi-task learning (MTL) to train the parser and the ORL model simultaneously. We verify our methods on the benchmark MPQA corpus. The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9.29 over the syntax-agnostic baseline. In addition, we find that the contributions from syntactic knowledge do not fully overlap with contextualized word representations (BERT). Our best model achieves 4.34 higher F1 score than the current state-ofthe-art.


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Hierarchy Response Learning for Neural Conversation Generation
Bo Zhang | Xiaoming Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The neural encoder-decoder models have shown great promise in neural conversation generation. However, they cannot perceive and express the intention effectively, and hence often generate dull and generic responses. Unlike past work that has focused on diversifying the output at word-level or discourse-level with a flat model to alleviate this problem, we propose a hierarchical generation model to capture the different levels of diversity using the conditional variational autoencoders. Specifically, a hierarchical response generation (HRG) framework is proposed to capture the conversation intention in a natural and coherent way. It has two modules, namely, an expression reconstruction model to capture the hierarchical correlation between expression and intention, and an expression attention model to effectively combine the expressions with contents. Finally, the training procedure of HRG is improved by introducing reconstruction loss. Experiment results show that our model can generate the responses with more appropriate content and expression.


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Supervised Treebank Conversion: Data and Approaches
Xinzhou Jiang | Zhenghua Li | Bo Zhang | Min Zhang | Sheng Li | Luo Si
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance. However, previous work mainly focuses on unsupervised treebank conversion and has made little progress due to the lack of manually labeled data where each sentence has two syntactic trees complying with two different guidelines at the same time, referred as bi-tree aligned data. In this work, we for the first time propose the task of supervised treebank conversion. First, we manually construct a bi-tree aligned dataset containing over ten thousand sentences. Then, we propose two simple yet effective conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser. Experimental results show that 1) the two conversion approaches achieve comparable conversion accuracy, and 2) treebank conversion is superior to the widely used multi-task learning framework in multi-treebank exploitation and leads to significantly higher parsing accuracy.


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Discriminative Deep Random Walk for Network Classification
Juzheng Li | Jun Zhu | Bo Zhang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields
Jingwei Zhuo | Yong Cao | Jun Zhu | Bo Zhang | Zaiqing Nie
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
Jun Zhu | Xun Zheng | Bo Zhang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Extracting and Visualizing Semantic Relationships from Chinese Biomedical Text
Qingliang Miao | Shu Zhang | Bo Zhang | Hao Yu
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation