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Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings
John Wieting
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Kevin Gimpel
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Paper Details:
Month: July
Year: 2017
Location: Vancouver, Canada
Venue:
ACL |
Citations
URL
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification
Han Guo
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Ramakanth Pasunuru
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Mohit Bansal
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Convolutional Neural Network for Universal Sentence Embeddings
Xiaoqi Jiao
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Fang Wang
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Dan Feng
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Evaluation of Unsupervised Compositional Representations
Hanan Aldarmaki
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Mona Diab
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Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
Wuwei Lan
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Wei Xu
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Picking Apart Story Salads
Su Wang
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Eric Holgate
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Greg Durrett
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Katrin Erk
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Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Ashutosh Baheti
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Alan Ritter
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Jiwei Li
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Bill Dolan
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Universal Language Model Fine-tuning for Text Classification
Jeremy Howard
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Sebastian Ruder
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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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Semantically Equivalent Adversarial Rules for Debugging NLP models
Marco Tulio Ribeiro
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Sameer Singh
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Carlos Guestrin
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Quality Signals in Generated Stories
Manasvi Sagarkar
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John Wieting
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Lifu Tu
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Kevin Gimpel
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Paraphrase Detection on Noisy Subtitles in Six Languages
Eetu Sjöblom
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Mathias Creutz
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Mikko Aulamo
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https://doi.org/10.18653/v1/P17-1190
https://doi.org/10.18653/v1/P17-1190
http://arxiv.org/abs/1605.02688
Field Of Study
Linguistic Trends
Embeddings
Task
Textual Entailment
Semantic Similarity
Machine Translation
Text Simplification
Approach
Unsupervised Learning
Language
Multilingual
English
Spanish
Dataset
News
Twitter
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