UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity
By Joe Barrow, Denis Peskov. 2017
7 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
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In Artificial Intelligence
- Daniel Matthew Cer
- Graham Neubig
- Iryna Gurevych
- Eneko Agirre
- Mona T. Diab
- John Wieting
- Kevin Gimpel
- Jimmy J. Lin
In Computer Vision and Pattern Recognition
- Iñigo Lopez-Gazpio
- Mona T. Diab
- Lucia Specia
- Eneko Agirre
- Daniel Matthew Cer
- Desmond Elliott
- Loïc Barrault
In Natural Language Processing
- John Wieting
- Kevin Gimpel
- Daniel Matthew Cer
- Kawin Ethayarajh
- Iryna Gurevych
- Mona T. Diab
- Eneko Agirre
- Nils Reimers
Citations in the corpus
- SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, methodology): “UMDeep (Barrow and Peskov, 2017) took a similar approach using LSTMs rather than CNNs for the sentence embeddings.”