Supervised Treebank Conversion: Data and Approaches

Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si


Abstract
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.
Anthology ID:
P18-1252
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2706–2716
Language:
URL:
https://aclanthology.org/P18-1252
DOI:
10.18653/v1/P18-1252
Bibkey:
Cite (ACL):
Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, and Luo Si. 2018. Supervised Treebank Conversion: Data and Approaches. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2706–2716, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Supervised Treebank Conversion: Data and Approaches (Jiang et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1252.pdf
Note:
 P18-1252.Notes.zip
Poster:
 P18-1252.Poster.pdf