Abstract
This is the Lump team participation at SemEval 2017 Task 1 on Semantic Textual Similarity. Our supervised model relies on features which are multilingual or interlingual in nature. We include lexical similarities, cross-language explicit semantic analysis, internal representations of multilingual neural networks and interlingual word embeddings. Our representations allow to use large datasets in language pairs with many instances to better classify instances in smaller language pairs avoiding the necessity of translating into a single language. Hence we can deal with all the languages in the task: Arabic, English, Spanish, and Turkish.- Anthology ID:
- S17-2019
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 144–149
- Language:
- URL:
- https://aclanthology.org/S17-2019
- DOI:
- 10.18653/v1/S17-2019
- Cite (ACL):
- Cristina España-Bonet and Alberto Barrón-Cedeño. 2017. Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 144–149, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity (España-Bonet & Barrón-Cedeño, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/S17-2019.pdf