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This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task.
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Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation
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Umd-ttic-uw at semeval-2016 task 1: Attention-based multi-perspective convolutional neural networks for textual similarity measurement
Hua He, John Wieting, Kevin Gimpel, Jinfeng Rao, and Jimmy Lin. 2016 · 2016
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Uta dlnlp at semeval-2016 task 1: Semantic textual similarity: A unified framework for semantic processing and evaluation
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Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability
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