Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training
Abstract
We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.- Anthology ID:
- C16-1121
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1275–1284
- Language:
- URL:
- https://aclanthology.org/C16-1121
- DOI:
- Cite (ACL):
- Quynh Ngoc Thi Do, Steven Bethard, and Marie-Francine Moens. 2016. Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1275–1284, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training (Do et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C16-1121.pdf