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Learning Generic Sentence Representations Using Convolutional Neural Networks
Zhe Gan
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Yunchen Pu
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Ricardo Henao
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Chunyuan Li
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Xiaodong He
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Lawrence Carin
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Paper Details:
Month: September
Year: 2017
Location: Copenhagen, Denmark
Venue:
EMNLP |
Citations
URL
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder
Minghua Zhang
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Yunfang Wu
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Weikang Li
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Wei Li
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A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak
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Nikunj Saunshi
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Yingyu Liang
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Tengyu Ma
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Brandon Stewart
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Sanjeev Arora
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Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Dinghan Shen
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Guoyin Wang
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Wenlin Wang
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Martin Renqiang Min
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Qinliang Su
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Yizhe Zhang
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Chunyuan Li
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Ricardo Henao
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Lawrence Carin
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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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SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
Taeuk Kim
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Jihun Choi
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Sang-goo Lee
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Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Shuai Tang
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Hailin Jin
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Chen Fang
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Zhaowen Wang
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Virginia de Sa
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No URLs Found
Field Of Study
Linguistic Trends
Embeddings
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Task
Machine Translation
Approach
Deep Learning
Language
English
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