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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S17-2014.pdf