Abstract
Translating prepositions is a difficult and under-studied problem in SMT. We present a novel method to improve the translation of prepositions by using noun classes to model their selectional preferences. We compare three variants of noun class information: (i) classes induced from the lexical resource GermaNet or obtained from clusterings based on either (ii) window information or (iii) syntactic features. Furthermore, we experiment with PP rule generalization. While we do not significantly improve over the baseline, our results demonstrate that (i) integrating selectional preferences as rigid class annotation in the parse tree is sub-optimal, and that (ii) clusterings based on window co-occurrence are more robust than syntax-based clusters or GermaNet classes for the task of modeling selectional preferences.- Anthology ID:
- 2014.amta-researchers.21
- Volume:
- Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
- Month:
- October 22-26
- Year:
- 2014
- Address:
- Vancouver, Canada
- Editors:
- Yaser Al-Onaizan, Michel Simard
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 275–287
- Language:
- URL:
- https://aclanthology.org/2014.amta-researchers.21
- DOI:
- Cite (ACL):
- Marion Weller, Sabine Schulte im Walde, and Alexander Fraser. 2014. Using noun class information to model selectional preferences for translating prepositions in SMT. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 275–287, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- Using noun class information to model selectional preferences for translating prepositions in SMT (Weller et al., AMTA 2014)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2014.amta-researchers.21.pdf