Abstract
This paper presents an overview of the NLPCC 2019 shared task on cross-domain dependency parsing, including (1) the data annotation process, (2) task settings, (3) methods, results, and analysis of submitted systems and our recent work (Li+19), (4) discussions on related works and future directions. Considering that unsupervised domain adaptation is very difficult and has made limited progress in the past decades, we for the first time setup semi-supervised subtasks that allow to use a few thousand target-domain labeled sentences for training. We provide about 17 K labeled sentences from a balanced corpus as the source domain (BC), and as three target domains 10 K sentences from product comments (PC), 8 K sentences from product blogs (PB), and 3 K sentences from the web fiction named “Zhuxian” (ZX). All information about this task can be found at http://hlt.suda.edu.cn/index.php/Nlpcc-2019-shared-task, including the data sharing agreement.
Supported by National Natural Science Foundation of China (Grant No. 61876116, 61525205).
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Notes
- 1.
Webpage for our treebank annotation: http://hlt.suda.edu.cn/index.php/SUCDT.
- 2.
The parser can be tried at http://hlt-la.suda.edu.cn.
- 3.
Our major purpose for annotating these datasets is to support supervised treebank conversion.
- 4.
The word embeddings are obtained by training word2vec on the Chinese Gigaword 3 and all the target-domain unlabeled data.
- 5.
Please notice again that semi-supervised in our domain adaptation scenario is about whether using target-domain labeled training data.
- 6.
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Peng, X., Li, Z., Zhang, M., Wang, R., Zhang, Y., Si, L. (2019). Overview of the NLPCC 2019 Shared Task: Cross-Domain Dependency Parsing. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_69
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