Chikara Hashimoto. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Intelligent assistants, such as Siri, are expected to converse comprehensibly with users. To facilitate improvement of their conversational ability, we have developed a method that detects absurd conversations recorded in intelligent assistant logs by identifying user feedback utterances that indicate users' favorable and unfavorable evaluations of intelligent assistant responses; e.g., "great!" is favorable, whereas "what are you talking about?" is unfavorable. Assuming that absurd/comprehensible conversations tend to be followed by unfavorable/favorable utterances, our method extracts some absurd/comprehensible conversations from the log to train a conversation classifier that sorts all the conversations recorded in the log as either absurd or not. The challenge is that user feedback utterances are often ambiguous; e.g., a user may give an unfavorable utterance (e.g., "don't be silly!") to a comprehensible conversation in which the intelligent assistant was attempting to make a joke. An utterance classifier is thus used to score the feedback utterances in accordance with how unambiguously they indicate absurdity. Experiments showed that our method significantly outperformed methods that lacked a conversation and/or utterance classifier, indicating the effectiveness of the two classifiers. Our method only requires user feedback utterances, which would be independent of domains. Experiments focused on chitchat, web search, and weather domains indicated that our method is likely domain-independent.

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.

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.

Event causality knowledge is indispensable for intelligent natural language understanding. The problem is that any method for extracting event causalities from text is insufficient; it is likely that some event causalities that we can recognize in this world are not written in a corpus, no matter its size. We propose a method of hypothesizing unseen event causalities from known event causalities extracted from the web by the semantic relations between nouns. For example, our method can hypothesize "deploy a security camera" -> "avoid crimes" from "deploy a mosquito net" -> "avoid malaria" through semantic relation . Our experiments show that, from 2.4 million event causalities extracted from the web, our method generated more than 300,000 hypotheses, which were not in the input, with 70% precision. We also show that our method outperforms a state-of-the-art hypothesis generation method.

In this work, we improve the performance of intra-sentential zero anaphora resolution in Japanese using a novel method of recognizing subject sharing relations.In Japanese, a large portion of intrasentential zero anaphora can be regarded as subject sharing relations between predicates, that is, the subject of some predicate is also the unrealized subject of other predicates.We develop an accurate recognizer of subject sharing relations for pairs of predicates in a single sentence, and then construct a subject shared predicate network, which is a set of predicates that are linked by the subject sharing relations recognized by our recognizer.We finally combine our zero anaphora resolution method exploiting the subject shared predicate network and a state-ofthe-art ILP-based zero anaphora resolution method.Our combined method achieved a significant improvement over the the ILPbased method alone on intra-sentential zero anaphora resolution in Japanese.To the best of our knowledge, this is the first work to explicitly use an independent subject sharing recognizer in zero anaphora resolution.

We propose a novel method for acquiring entailment pairs of binary patterns on a large-scale.This method exploits the transitivity of entailment and a self-training scheme to improve the performance of an already strong supervised classifier for entailment, and unlike previous methods that exploit transitivity, it works on a largescale.With it we acquired 138.1 million pattern pairs with 70% precision with such non-trivial lexical substitution as "use Y to distribute X"→"X is available on Y" whose extraction is considered difficult.This represents 50.4 million more pattern pairs (a 57.5% increase) than what our supervised baseline extracted at the same precision.

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture→exacerbate desertification from the web using se-mantic relation (between nouns), context, and association features. Experiments show that our method outperforms base-lines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like conduct slash-and-burn agriculture→exacerbate desertification→increase Asian dust (from China)→asthma gets worse. Experi-ments show that we can generate 50,000 scenarios with 68 % precision. We also generated a scenario deforestation con-tinues→global warming worsens→sea temperatures rise→vibrio parahaemolyti-cus fouls (water), which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we “predicted ” the future event sequence in a sense. 1

Julien Kloetzer, Stijn De Saeger, Kentaro Torisawa, Chikara Hashimoto, Jong-Hoon Oh, Motoki Sano, Kiyonori Ohtake. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013.