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Ryu Iida

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3 papers

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AAAI Conference 2019 Conference Paper

Exploiting Background Knowledge in Compact Answer Generation for Why-Questions

Ryu Iida
Canasai Kruengkrai
Ryo Ishida
Kentaro Torisawa
Jong-Hoon Oh
Julien Kloetzer

This paper proposes a novel method for generating compact answers to open-domain why-questions, such as the following answer, “Because deep learning technologies were introduced, ” to the question, “Why did Google’s machine translation service improve so drastically? ” Although many works have dealt with why-question answering, most have focused on retrieving as answers relatively long text passages that consist of several sentences. Because of their length, such passages are not appropriate to be read aloud by spoken dialog systems and smart speakers; hence, we need to create a method that generates compact answers. We developed a novel neural summarizer for this compact answer generation task. It combines a recurrent neural network-based encoderdecoder model with stacked convolutional neural networks and was designed to effectively exploit background knowledge, in this case a set of causal relations (e. g. , “[Microsoft’s machine translation has made great progress over the last few years]effect since [it started to use deep learning. ]cause”) that was extracted from a large web data archive (4 billion web pages). Our experimental results show that our method achieved significantly better ROUGE F-scores than existing encoder-decoder models and their variations that were augmented with query-attention and memory networks, which are used to exploit the background knowledge.

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AAAI Conference 2018 Conference Paper

Semi-Distantly Supervised Neural Model for Generating Compact Answers to Open-Domain Why Questions

Ryo Ishida
Kentaro Torisawa
Jong-Hoon Oh
Ryu Iida
Canasai Kruengkrai
Julien Kloetzer

This paper proposes a neural network-based method for generating compact answers to open-domain why-questions (e. g. , “Why was Mr. Trump elected as the president of the US? ”). Unlike factoid question answering methods that provide short text spans as answers, existing work for whyquestion answering have aimed at answering questions by retrieving relatively long text passages, each of which often consists of several sentences, from a text archive. While the actual answer to a why-question may be expressed over several consecutive sentences, these often contain redundant and/or unrelated parts. Such answers would not be suitable for spoken dialog systems and smart speakers such as Amazon Echo, which receive much attention in these days. In this work, we aim at generating non-redundant compact answers to why-questions from answer passages retrieved from a very large web data corpora (4 billion web pages) by an already existing open-domain why-question answering system, using a novel neural network obtained by extending existing summarization methods. We also automatically generate training data using a large number of causal relations automatically extracted from 4 billion web pages by an existing supervised causality recognizer. The data is used to train our neural network, together with manually created training data. Through a series of experiments, we show that both our novel neural network and auto-generated training data improve the quality of the generated answers both in ROUGE score and in a subjective evaluation.

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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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