Abstract
We use referential translation machines for predicting the semantic similarity of text in all STS tasks which contain Arabic, English, Spanish, and Turkish this year. RTMs pioneer a language independent approach to semantic similarity and remove the need to access any task or domain specific information or resource. RTMs become 6th out of 52 submissions in Spanish to English STS. We average prediction scores using weights based on the training performance to improve the overall performance.- Anthology ID:
- S17-2030
- 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:
- 203–207
- Language:
- URL:
- https://aclanthology.org/S17-2030
- DOI:
- 10.18653/v1/S17-2030
- Cite (ACL):
- Ergun Biçici. 2017. RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 203–207, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity (Biçici, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S17-2030.pdf