@inproceedings{wang-etal-2018-multi-granularity,
title = "Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering",
author = "Wang, Wei and
Yan, Ming and
Wu, Chen",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1158",
doi = "10.18653/v1/P18-1158",
pages = "1705--1714",
abstract = "This paper describes a novel hierarchical attention network for reading comprehension style question answering, which aims to answer questions for a given narrative paragraph. In the proposed method, attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph. Specifically, it first encode the question and paragraph with fine-grained language embeddings, to better capture the respective representations at semantic level. Then it proposes a multi-granularity fusion approach to fully fuse information from both global and attended representations. Finally, it introduces a hierarchical attention network to focuses on the answer span progressively with multi-level soft-alignment. Extensive experiments on the large-scale SQuAD, TriviaQA dataset validate the effectiveness of the proposed method. At the time of writing the paper, our model achieves state-of-the-art on the both SQuAD and TriviaQA Wiki leaderboard as well as two adversarial SQuAD datasets.",
}
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%0 Conference Proceedings
%T Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering
%A Wang, Wei
%A Yan, Ming
%A Wu, Chen
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wang-etal-2018-multi-granularity
%X This paper describes a novel hierarchical attention network for reading comprehension style question answering, which aims to answer questions for a given narrative paragraph. In the proposed method, attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph. Specifically, it first encode the question and paragraph with fine-grained language embeddings, to better capture the respective representations at semantic level. Then it proposes a multi-granularity fusion approach to fully fuse information from both global and attended representations. Finally, it introduces a hierarchical attention network to focuses on the answer span progressively with multi-level soft-alignment. Extensive experiments on the large-scale SQuAD, TriviaQA dataset validate the effectiveness of the proposed method. At the time of writing the paper, our model achieves state-of-the-art on the both SQuAD and TriviaQA Wiki leaderboard as well as two adversarial SQuAD datasets.
%R 10.18653/v1/P18-1158
%U https://aclanthology.org/P18-1158
%U https://doi.org/10.18653/v1/P18-1158
%P 1705-1714
Markdown (Informal)
[Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering](https://aclanthology.org/P18-1158) (Wang et al., ACL 2018)
ACL