Enhancing Recurrent Neural Networks with Positional Attention for Question Answering-Reference-Cited by-同舟云学术

Enhancing Recurrent Neural Networks with Positional Attention for Question Answering

Published:2017-08-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Chen Qin¹,Hu Qinmin¹,Huang Jimmy Xiangji²,He Liang³,An Weijie¹

Affiliation:

1. East China Normal University, Shanghai, China

2. York University, Toronto, Canada

3. East China Normal University & Shanghai Engineering Research Center of Intelligent Service Robot, Shanghai, China

Funder

Science and Technology Commission of Shanghai Municipality

NSERC CREATE award

National Key Technology Support Program

Shanghai Municipal Commission of Economy and Information Under Grant Project

Discovery grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3077136.3080699

Reference17 articles.

1. Framewise phoneme classification with bidirectional LSTM and other neural network architectures

2. Baiyan Liu Xiangdong An and Jimmy Xiangji Huang. 2015. Using term location information to enhance probabilistic information retrieval. SIGIR. 883--886. Baiyan Liu Xiangdong An and Jimmy Xiangji Huang. 2015. Using term location information to enhance probabilistic information retrieval. SIGIR. 883--886.

3. Jonas Mueller and Aditya Thyagarajan. 2016. Siamese Recurrent Architectures for Learning Sentence Similarity. AAAI. 2786--2792. Jonas Mueller and Aditya Thyagarajan. 2016. Siamese Recurrent Architectures for Learning Sentence Similarity. AAAI. 2786--2792.

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