Abstract
Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.- Anthology ID:
- I17-1084
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 832–842
- Language:
- URL:
- https://aclanthology.org/I17-1084
- DOI:
- Cite (ACL):
- Sheng Zhang, Kevin Duh, and Benjamin Van Durme. 2017. Selective Decoding for Cross-lingual Open Information Extraction. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 832–842, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Selective Decoding for Cross-lingual Open Information Extraction (Zhang et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/I17-1084.pdf