UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity
Joe Barrow, Denis Peskov · 2017
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.