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[1611.01603] Bidirectional Attention Flow for Machine Comprehension

 3 years ago
source link: https://arxiv.org/abs/1611.01603
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Computer Science > Computation and Language

[Submitted on 5 Nov 2016 (v1), last revised 21 Jun 2018 (this version, v6)]

Bidirectional Attention Flow for Machine Comprehension

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Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it with a fixed-size vector, couple attentions temporally, and/or often form a uni-directional attention. In this paper we introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. Our experimental evaluations show that our model achieves the state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze test.

Comments: Published as a conference paper at ICLR 2017 Subjects: Computation and Language (cs.CL) Cite as: arXiv:1611.01603 [cs.CL]   (or arXiv:1611.01603v6 [cs.CL] for this version)   https://doi.org/10.48550/arXiv.1611.01603

Submission history

From: Minjoon Seo [view email]
[v1] Sat, 5 Nov 2016 04:49:00 UTC (1,009 KB)
[v2] Mon, 21 Nov 2016 21:34:22 UTC (1,010 KB)
[v3] Tue, 29 Nov 2016 18:46:50 UTC (1,011 KB)
[v4] Tue, 7 Feb 2017 22:01:42 UTC (1,018 KB)
[v5] Fri, 24 Feb 2017 19:57:53 UTC (1,017 KB)
[v6] Thu, 21 Jun 2018 10:53:20 UTC (1,301 KB)

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