A Semi-Supervised Learning Approach to Why-Question Answering

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.

Authors

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

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