DT Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output
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.
Publication Title
Proceedings of the Annual Meeting of the Association for Computational Linguistics
Recommended Citation
Maharjan, N., Banjade, R., Gautam, D., Tamang, L., & Rus, V. (2017). DT Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 120-124. Retrieved from https://digitalcommons.memphis.edu/facpubs/20132
