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Towards Universal Paraphrastic Sentence Embeddings

Towards Universal Paraphrastic Sentence Embeddings

25 November 2015
John Wieting
Joey Tianyi Zhou
Kevin Gimpel
Karen Livescu
    AI4TS
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Papers citing "Towards Universal Paraphrastic Sentence Embeddings"

19 / 69 papers shown
Title
Latent Semantic Analysis Approach for Document Summarization Based on
  Word Embeddings
Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings
Kamal Al-Sabahi
Zuping Zhang
Kang Yang
19
9
0
08 Jul 2018
Neural Network Models for Paraphrase Identification, Semantic Textual
  Similarity, Natural Language Inference, and Question Answering
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
Wuwei Lan
Wei-ping Xu
20
121
0
12 Jun 2018
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature
  Vectors
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
M. Khodak
Nikunj Saunshi
Yingyu Liang
Tengyu Ma
Brandon M Stewart
Sanjeev Arora
38
98
0
14 May 2018
Dynamic Meta-Embeddings for Improved Sentence Representations
Dynamic Meta-Embeddings for Improved Sentence Representations
Douwe Kiela
Changhan Wang
Kyunghyun Cho
AI4TS
28
108
0
21 Apr 2018
Learning General Purpose Distributed Sentence Representations via Large
  Scale Multi-task Learning
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
Sandeep Subramanian
Adam Trischler
Yoshua Bengio
C. Pal
SSL
27
328
0
30 Mar 2018
SentEval: An Evaluation Toolkit for Universal Sentence Representations
SentEval: An Evaluation Toolkit for Universal Sentence Representations
Alexis Conneau
Douwe Kiela
37
630
0
14 Mar 2018
Modelling Domain Relationships for Transfer Learning on Retrieval-based
  Question Answering Systems in E-commerce
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce
Jianfei Yu
Minghui Qiu
Jing Jiang
Jun Huang
Shuangyong Song
Wei Chu
Haiqing Chen
23
101
0
23 Nov 2017
Semantic speech retrieval with a visually grounded model of
  untranscribed speech
Semantic speech retrieval with a visually grounded model of untranscribed speech
Herman Kamper
Gregory Shakhnarovich
Karen Livescu
29
53
0
05 Oct 2017
Learned in Translation: Contextualized Word Vectors
Learned in Translation: Contextualized Word Vectors
Bryan McCann
James Bradbury
Caiming Xiong
R. Socher
62
907
0
01 Aug 2017
Supervised Learning of Universal Sentence Representations from Natural
  Language Inference Data
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Alexis Conneau
Douwe Kiela
Holger Schwenk
Loïc Barrault
Antoine Bordes
AI4TS
SSL
49
2,093
0
05 May 2017
Learning to Generate Reviews and Discovering Sentiment
Learning to Generate Reviews and Discovering Sentiment
Alec Radford
Rafal Jozefowicz
Ilya Sutskever
44
504
0
05 Apr 2017
A practical approach to dialogue response generation in closed domains
A practical approach to dialogue response generation in closed domains
Y. Lu
Phillip Keung
Shaonan Zhang
Jason Sun
Vikas Bhardwaj
33
16
0
28 Mar 2017
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram
  Features
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Matteo Pagliardini
Prakhar Gupta
Martin Jaggi
SSL
20
692
0
07 Mar 2017
Learning Generic Sentence Representations Using Convolutional Neural
  Networks
Learning Generic Sentence Representations Using Convolutional Neural Networks
Zhe Gan
Yunchen Pu
Ricardo Henao
Chunyuan Li
Xiaodong He
Lawrence Carin
SSL
34
98
0
23 Nov 2016
Multi-view Recurrent Neural Acoustic Word Embeddings
Multi-view Recurrent Neural Acoustic Word Embeddings
Wanjia He
Weiran Wang
Karen Livescu
21
84
0
14 Nov 2016
Geometry of Polysemy
Geometry of Polysemy
Jiaqi Mu
S. Bhat
Pramod Viswanath
25
21
0
24 Oct 2016
An Empirical Evaluation of doc2vec with Practical Insights into Document
  Embedding Generation
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
Jey Han Lau
Timothy Baldwin
15
650
0
19 Jul 2016
Charagram: Embedding Words and Sentences via Character n-grams
Charagram: Embedding Words and Sentences via Character n-grams
John Wieting
Joey Tianyi Zhou
Kevin Gimpel
Karen Livescu
NAI
GNN
24
193
0
10 Jul 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
17
16,646
0
16 Feb 2016
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