@inproceedings{ghaeini-etal-2018-dependent,
title = "Dependent Gated Reading for Cloze-Style Question Answering",
author = "Ghaeini, Reza and
Fern, Xiaoli and
Shahbazi, Hamed and
Tadepalli, Prasad",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1282",
pages = "3330--3345",
abstract = "We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel dependent gated reading bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children{'}s Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.",
}
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<abstract>We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel dependent gated reading bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children’s Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.</abstract>
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%0 Conference Proceedings
%T Dependent Gated Reading for Cloze-Style Question Answering
%A Ghaeini, Reza
%A Fern, Xiaoli
%A Shahbazi, Hamed
%A Tadepalli, Prasad
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F ghaeini-etal-2018-dependent
%X We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel dependent gated reading bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children’s Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.
%U https://aclanthology.org/C18-1282
%P 3330-3345
Markdown (Informal)
[Dependent Gated Reading for Cloze-Style Question Answering](https://aclanthology.org/C18-1282) (Ghaeini et al., COLING 2018)
ACL
- Reza Ghaeini, Xiaoli Fern, Hamed Shahbazi, and Prasad Tadepalli. 2018. Dependent Gated Reading for Cloze-Style Question Answering. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3330–3345, Santa Fe, New Mexico, USA. Association for Computational Linguistics.