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Masahiro Tanaka

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2 papers

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AAAI Conference 2017 Conference Paper

Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks

Canasai Kruengkrai
Kentaro Torisawa
Chikara Hashimoto
Julien Kloetzer
Jong-Hoon Oh
Masahiro Tanaka

We propose a method for recognizing such event causalities as “smoke cigarettes” → “die of lung cancer” using background knowledge taken from web texts as well as original sentences from which candidates for the causalities were extracted. We retrieve texts related to our event causality candidates from four billion web pages by three distinct methods, including a why-question answering system, and feed them to our multi-column convolutional neural networks. This allows us to identify the useful background knowledge scattered in web texts and effectively exploit the identiﬁed knowledge to recognize event causalities. We empirically show that the combination of our neural network architecture and background knowledge signiﬁcantly improves average precision, while the previous state-of-the-art method gains just a small beneﬁt from such background knowledge.

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AAAI Conference 2016 Conference Paper

A Semi-Supervised Learning Approach to Why-Question Answering

Jong-Hoon Oh
Kentaro Torisawa
Chikara Hashimoto
Ryu Iida
Masahiro Tanaka
Julien Kloetzer

We propose a semi-supervised learning method for improving why-question answering (why-QA). The key of our method is to generate training data (question-answer pairs) from causal relations in texts such as “[Tsunamis are generated]effect because [the ocean’s water mass is displaced by an earthquake]cause. ” A naive method for the generation would be to make a question-answer pair by simply converting the effect part of the causal relations into a why-question, like “Why are tsunamis generated? ” from the above example, and using the source text of the causal relations as an answer. However, in our preliminary experiments, this naive method actually failed to improve the why-QA performance. The main reason was that the machine-generated questions were often incomprehensible like “Why does (it) happen? ”, and that the system suffered from overﬁtting to the results of our automatic causality recognizer. Hence, we developed a novel method that effectively ﬁlters out incomprehensible questions and retrieves from texts answers that are likely to be paraphrases of a given causal relation. Through a series of experiments, we showed that our approach significantly improved the precision of the top answer by 8% over the current state-of-the-art system for Japanese why-QA.

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