DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output
Nabin Maharjan, Rajendra Banjade, Dipesh Gautam, Lasang J. Tamang, Vasile Rus
Abstract
We describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity scores calculated using word and chunk alignments, word/sentence embeddings, and Gaussian Mixture Model(GMM). The correlation between our system’s output and the human judgments were up to 0.8536, which is more than 10% above baseline, and almost as good as the best performing system which was at 0.8547 correlation (the difference is just about 0.1%). Also, our system produced leading results when evaluated with a separate STS benchmark dataset. The word alignment and sentence embeddings based features were found to be very effective.- Anthology ID:
- S17-2014
- 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:
- 120–124
- Language:
- URL:
- https://aclanthology.org/S17-2014
- DOI:
- 10.18653/v1/S17-2014
- Cite (ACL):
- Nabin Maharjan, Rajendra Banjade, Dipesh Gautam, Lasang J. Tamang, and Vasile Rus. 2017. DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 120–124, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output (Maharjan et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/S17-2014.pdf