NDLI: Multi-Column Convolutional Neural Networks with Causality-Attention for Why-Question Answering

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Multi-Column Convolutional Neural Networks with Causality-Attention for Why-Question Answering

Multi-Column Convolutional Neural Networks with Causality-Attention for Why-Question Answering

Content Provider	ACM Digital Library
Author	Torisawa, Kentaro Iida, Ryu Kruengkrai, Canasai Kloetzer, Julien Oh, Jong-Hoon
Abstract	Why-question answering (why-QA) is a task to retrieve answers (or answer passages) to why-questions (e.g., "why are tsunamis generated?") from a text archive. Several previously proposed methods for why-QA improved their performance by automatically recognizing causalities that are expressed with such explicit cues as "because" in answer passages and using the recognized causalities as a clue for finding proper answers. However, in answer passages, causalities might be implicitly expressed, (i.e., without any explicit cues): "An earthquake suddenly displaced sea water and a tsunami was generated." The previous works did not deal with such implicitly expressed causalities and failed to find proper answers that included the causalities. We improve why-QA based on the following two ideas. First, implicitly expressed causalities in one text might be expressed in other texts with explicit cues. If we can automatically recognize such explicitly expressed causalities from a text archive and use them to complement the implicitly expressed causalities in an answer passage, we can improve why-QA. Second, the causes of similar events tend to be described with a similar set of words (e.g., "seismic energy" and "tectonic plates" for "the Great East Japan Earthquake" and "the 1906 San Francisco Earthquake"). As such, even if we cannot find in a text archive any explicitly expressed cause of an event (e.g., "the Great East Japan Earthquake") expressed in a question (e.g., "Why did the Great East Japan earthquake happen?"), we might be able to identify its implicitly expressed causes with a set of words (e.g., "tectonic plates") that appear in the explicitly expressed cause of a similar event (e.g., "the 1906 San Francisco Earthquake"). We implemented these two ideas in our multi-column convolutional neural networks with a novel attention mechanism, which we call causality attention. Through experiments on Japanese why-QA, we confirmed that our proposed method outperformed the state-of-the-art systems.
Starting Page	415
Ending Page	424
Page Count	10
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018737
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Neural attention Convolutional neural network Question answering Why-question answering Causality
Content Type	Text
Resource Type	Article

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