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Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa · 2011
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
Learning multi-relational semantics using neural-embedding models
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
Earlier work this paper cites.
Finding function in form: Compositional character models for open vocabulary word representation
Wang Ling, Tiago Luís, Luís Marujo, Ramón Fernandez Astudillo, Silvio Amir, Chris Dyer, Alan W Black, and Isabel Trancoso · 2015
Earlier work this paper cites.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Earlier work this paper cites.
A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning · 2016
Cited alongside, same era.
Attention-over-attention neural networks for reading comprehension
Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu · 2016
Cited alongside, same era.
The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston · 2016
Cited alongside, same era.
Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst · 2016
Cited alongside, same era.
Character-aware neural language models
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M Rush · 2016
Cited alongside, same era.
Tsendsuren Munkhdalai and Hong Yu · 2016
Closest in time.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Iterative alternating neural attention for machine reading
Alessandro Sordoni, Phillip Bachman, and Yoshua Bengio · 2016
Closest in time.
Natural language comprehension with the epireader
Adam Trischler, Zheng Ye, Xingdi Yuan, and Kaheer Suleman · 2016
Closest in time.
Machine comprehension using match-lstm and answer pointer
Shuohang Wang and Jing Jiang · 2016
Closest in time.
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Minh-Thang Luong and Christopher D Manning · 2016
Cited alongside, same era.
Gated word-character recurrent language model
Yasumasa Miyamoto and Kyunghyun Cho · 2016
Cited alongside, same era.
Gated-attention readers for text comprehension
Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov
Cited in the paper.
Tweet2vec: Character-based distributed representations for social media
Bhuwan Dhingra, Zhong Zhou, Dylan Fitzpatrick, Michael Muehl, and William W Cohen
Cited in the paper.
Multi-task cross-lingual sequence tagging from scratch
Zhilin Yang, Ruslan Salakhutdinov, and William Cohen
Cited in the paper.
Review networks for caption generation
Zhilin Yang, Ye Yuan, Yuexin Wu, Ruslan Salakhutdinov, and William W Cohen
Cited in the paper.
On multiplicative integration with recurrent neural networks
Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, and Ruslan Salakhutdinov · 2016
Closest in time.
End-to-end answer chunk extraction and ranking for reading comprehension
Yang Yu, Wei Zhang, Kazi Hasan, Mo Yu, Bing Xiang, and Bowen Zhou · 2016
Closest in time.