Abstract
To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16% in primary track.- Anthology ID:
- S17-2028
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 191–197
- Language:
- URL:
- https://aclanthology.org/S17-2028
- DOI:
- 10.18653/v1/S17-2028
- Cite (ACL):
- Junfeng Tian, Zhiheng Zhou, Man Lan, and Yuanbin Wu. 2017. ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 191–197, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity (Tian et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S17-2028.pdf