A dialog system that can monitor the health status of seniors has a huge potential for solving the labor force shortage in the caregiving industry in aging societies. As a part of efforts to create such a system, we are developing two modules that are aimed to correctly interpret user utterances: (i) a yes/no response classifier, which categorizes responses to health-related yes/no questions that the system asks; and (ii) an entailment recognizer, which detects users’ voluntary mentions about their health status. To apply machine learning approaches to the development of the modules, we created large annotated datasets of 280,467 question-response pairs and 38,868 voluntary utterances. For question-response pairs, we asked annotators to avoid direct “yes” or “no” answers, so that our data could cover a wide range of possible natural language responses. The two modules were implemented by fine-tuning a BERT model, which is a recent successful neural network model. For the yes/no response classifier, the macro-average of the average precisions (APs) over all of our four categories (Yes/No/Unknown/Other) was 82.6% (96.3% for “yes” responses and 91.8% for “no” responses), while for the entailment recognizer it was 89.9%.

Recently, many people express their opinions using social networking services such as Twitter and Facebook. Each opinion has a stance related to something such as product, service, and politics. The task of detecting a stance is known as sentiment analysis, reputation mining, and stance detection. A popular approach for stance detection uses sentiment polarity towards a target in a text. This approach is known as targeted sentiment analysis. If a target appears in text, the detecting stance based on targeted sentiment polarity would work well. However, how can we detect stance towards an event? (e.g. "I cannot understand why man can marry only with a woman", "The problem of low birth rate becomes more severe" to the event "Allowing same-sex marriage"). To detect these stances, it is necessary to recognize a situation in which the event occurs or does not occur. To classify texts including these phenomena, we propose a classification method based on machine learning considering PRIOR-SITUATION and EFFECT.

Since the problem of textual entailment recognition requires capturing semantic relations between diverse expressions of language, linguistic and world knowledge play an important role. In this article, we explore the effectiveness of different types of currently available resources including synonyms, antonyms, hypernym-hyponym relations, and lexical entailment relations for the task of textual entailment recognition. In order to do so, we develop an entailment relation recognition system which utilizes diverse linguistic analyses and resources to align the linguistic units in a pair of texts and identifies entailment relations based on these alignments. We use the Japanese subset of the NTCIR-9 RITE-1 dataset for evaluation and error analysis, conducting ablation testing and evaluation on hand-crafted alignment gold standard data to evaluate the contribution of individual resources. Error analysis shows that existing knowledge sources are effective for RTE, but that their coverage is limited, especially for domain-specific and other low-frequency expressions. To increase alignment coverage on such expressions, we propose a method of alignment inference that uses syntactic and semantic dependency information to identify likely alignments without relying on external resources. Evaluation adding alignment inference to a system using all available knowledge sources shows improvements in both precision and recall of entailment relation recognition.

Given podcasts (audio blogs) that are sets of speech files called episodes, this paper describes a method for retrieving episodes that have similar content. Although most previous retrieval methods were based on bibliographic information, tags, or users' playback behaviors without considering spoken content, our method can compute content-based similarity based on speech recognition results of podcast episodes even if the recognition results include some errors. To overcome those errors, it converts intermediate speech-recognition results to a confusion network containing competitive candidates, and then computes the similarity by using keywords extracted from the network. Experimental results with episodes that have different word accuracy and content showed that keywords obtained from competitive candidates were useful in retrieving similar episodes. To show relevant episodes, our method will be incorporated into PodCastle, a public web service that provides full-text searching of podcasts on the basis of speech recognition.