Feng Jiang


2020

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融合全局和局部信息的汉语宏观篇章结构识别(Combining Global and Local Information to Recognize Chinese Macro Discourse Structure)
Yaxin Fan (范亚鑫) | Feng Jiang (蒋峰) | Xiaomin Chu (褚晓敏) | Peifeng Li (李培峰) | Qiaoming Zhu (朱巧明)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

作为宏观篇章分析中的基础任务,篇章结构识别任务的目的是识别相邻篇章单元之间的结构,并层次化构建篇章结构树。已有的工作只考虑局部的结构和语义信息或只考虑全局信息。因此,本文提出了一种融合全局和局部信息的指针网络模型,该模型在考虑全局的语义信息同时,又考虑局部段落间的语义关系密切程度,从而有效地提高宏观篇章结构识别的能力。在汉语宏观篇章树库(MCDTB)的实验结果表明,本文所提出的模型性能优于目前性能最好的模型。

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Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading
Feng Jiang | Xiaomin Chu | Peifeng Li | Fang Kong | Qiaoming Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English. Due to the cultural and linguistic differences, existing successful methods on English discourse parsing cannot be transformed into Chinese directly, especially in paragraph level suffering from longer discourse units and fewer explicit connectives. To alleviate the above issues, we propose two reading modes, i.e., the global backward reading and the local reverse reading, to construct Chinese paragraph level discourse trees. The former processes discourse units from the end to the beginning in a document to utilize the left-branching bias of discourse structure in Chinese, while the latter reverses the position of paragraphs in a discourse unit to enhance the differentiation of coherence between adjacent discourse units. The experimental results on Chinese MCDTB demonstrate that our model outperforms all strong baselines.

2019

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Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Shuang Chen | Jinpeng Wang | Xiaocheng Feng | Feng Jiang | Bing Qin | Chin-Yew Lin
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge. They often assume that writing knowledge can be acquired from the training data alone. However, when people are writing, they not only rely on the data but also consider related knowledge. In this paper, we enhance neural data-to-text models with external knowledge in a simple but effective way to improve the fidelity of generated text. Besides relying on parallel data and text as in previous work, our model attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words. This allows the model to infer relevant facts which are not explicitly stated in the data table from an external knowledge source. Experimental results on twenty-one Wikipedia infobox-to-text datasets show our model, KBAtt, consistently improves a state-of-the-art model on most of the datasets. In addition, to quantify when and why external knowledge is effective, we design a metric, KBGain, which shows a strong correlation with the observed performance boost. This result demonstrates the relevance of external knowledge and sparseness of original data are the main factors affecting system performance.

2018

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Building a Macro Chinese Discourse Treebank
Xiaomin Chu | Feng Jiang | Sheng Xu | Qiaoming Zhu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank
Xiaomin Chu | Feng Jiang | Yi Zhou | Guodong Zhou | Qiaoming Zhu
Proceedings of the 27th International Conference on Computational Linguistics

Discourse parsing is a challenging task and plays a critical role in discourse analysis. This paper focus on the macro level discourse structure analysis, which has been less studied in the previous researches. We explore a macro discourse structure presentation schema to present the macro level discourse structure, and propose a corresponding corpus, named Macro Chinese Discourse Treebank. On these bases, we concentrate on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition. In order to reduce the error transmission between the associated tasks, we adopt a joint model of the two tasks, and an Integer Linear Programming approach is proposed to achieve global optimization with various kinds of constraints.

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MCDTB: A Macro-level Chinese Discourse TreeBank
Feng Jiang | Sheng Xu | Xiaomin Chu | Peifeng Li | Qiaoming Zhu | Guodong Zhou
Proceedings of the 27th International Conference on Computational Linguistics

In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus. Fol-lowing RST (Rhetorical Structure Theory), we annotate the macro discourse information, including discourse structure, nuclearity and relationship, and the additional discourse information, including topic sentences, lead and abstract, to make the macro discourse annotation more objective and accurate. Finally, we annotated 720 articles with a Kappa value greater than 0.6. Preliminary experiments on this corpus verify the computability of MCDTB.

2004

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An Enhanced Model for Chinese Word Segmentation and Part-of-Speech Tagging
Feng Jiang | Hui Liu | Yuquan Chen | Ruzhan Lu
Proceedings of the Third SIGHAN Workshop on Chinese Language Processing