Abstract
Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called Halo, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings.- Anthology ID:
- S18-2017
- Volume:
- Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Malvina Nissim, Jonathan Berant, Alessandro Lenci
- Venue:
- *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 142–147
- Language:
- URL:
- https://aclanthology.org/S18-2017
- DOI:
- 10.18653/v1/S18-2017
- Cite (ACL):
- Hongyuan Mei, Sheng Zhang, Kevin Duh, and Benjamin Van Durme. 2018. Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 142–147, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction (Mei et al., *SEM 2018)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/S18-2017.pdf