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HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity
Yang Shao
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Paper Details:
Month: August
Year: 2017
Location: Vancouver, Canada
Venue:
*SEMEVAL |
Citations
URL
Universal Sentence Encoder for English
Daniel Cer
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Yinfei Yang
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Sheng-yi Kong
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Nan Hua
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Nicole Limtiaco
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Rhomni St. John
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Noah Constant
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Mario Guajardo-Cespedes
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Steve Yuan
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Chris Tar
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Brian Strope
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Ray Kurzweil
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ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
John Wieting
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Kevin Gimpel
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Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline
Kawin Ethayarajh
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Learning Semantic Textual Similarity from Conversations
Yinfei Yang
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Steve Yuan
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Daniel Cer
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Sheng-yi Kong
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Noah Constant
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Petr Pilar
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Heming Ge
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Yun-Hsuan Sung
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Brian Strope
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Ray Kurzweil
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Rule-based vs. Neural Net Approaches to Semantic Textual Similarity
Linrui Zhang
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Dan Moldovan
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http://github.com/fchollet/keras
http://github.com/tensorflow/tensorflow
http://translate.google.com
http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark
http://www.aclweb.org/anthology/S17-2001
Field Of Study
Linguistic Trends
Embeddings
Task
Semantic Similarity
Machine Translation
Language
English
Spanish
Arabic
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