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Semi-Supervised Dependency Parsing with Arc-Factored Variational Autoencoding | |
2020-12 | |
会议录名称 | THE 28TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS (COLING) |
发表状态 | 正式接收 |
DOI | / |
摘要 | Mannual annotation for dependency parsing is both labourious and time costly, resulting in the difficulty to learn practical dependency parsers for many languages due to the lack of labelled training corpora. To compensate for the scarcity of labelled data, semi-supervised dependency parsing methods are developed to utilize unlabelled data in the training procedure of dependency parsers. In previous work, the autoencoder framework is a prevalent approach for the utilization of unlabelled data. In this framework, training sentences are reconstructed from a decoder conditioned on dependency trees predicted by an encoder. The tree structure requirement brings challenges for both the encoder and the decoder. Sophisticated techniques are employed to tackle these challenges at the expense of model complexity and approximations in encoding and decoding. In this paper, we propose a model based on the variational autoencoder framework. By relaxing the tree constraint in both the encoder and the decoder during training, we make the learning of our model fully arc-factored and thus circumvent the challenges brought by the tree constraint. We evaluate our model on datasets across several languages and the results demonstrate the advantage of our model over previous approaches in both parsing accuracy and speed. |
会议名称 | the 28th International Conference on Computational Linguistics (COLING) |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/124038 |
专题 | 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_屠可伟组 |
作者单位 | 1.School of Information Science and Technology, Shanghaitech University 2.Shanghai Engineering Research Center of Intelligent Vision and Imaging 3.Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences 4.University of Chinese Academy of Sciences |
第一作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Wang, Ge,Tu, Kewei. Semi-Supervised Dependency Parsing with Arc-Factored Variational Autoencoding[C],2020. |
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