@InProceedings{barrow-peskov:2017:SemEval,
  author    = {Barrow, Joe  and  Peskov, Denis},
  title     = {UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {180--184},
  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.},
  url       = {http://www.aclweb.org/anthology/S17-2026}
}