Abstract
We describe a modified shared-LSTM network for the Semantic Textual Similarity (STS) task at SemEval-2017. The network builds on previously explored Siamese network architectures. We treat max sentence length as an additional hyperparameter to be tuned (beyond learning rate, regularization, and dropout). Our results demonstrate that hand-tuning max sentence training length significantly improves final accuracy. After optimizing hyperparameters, we train the network on the multilingual semantic similarity task using pre-translated sentences. We achieved a correlation of 0.4792 for all the subtasks. We achieved the fourth highest team correlation for Task 4b, which was our best relative placement.- Anthology ID:
- S17-2026
- 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:
- 180–184
- Language:
- URL:
- https://aclanthology.org/S17-2026
- DOI:
- 10.18653/v1/S17-2026
- Cite (ACL):
- Joe Barrow and Denis Peskov. 2017. UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 180–184, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity (Barrow & Peskov, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/S17-2026.pdf