Demand for massive language resources is increasing as the data-driven approach has established a leading position in Natural Language Processing. However, creating dialogue corpora is still a difficult task due to the complexity of the human dialogue structure and the diversity of dialogue topics. Though crowdsourcing is majorly used to assemble such data, it presents problems such as less-motivated workers. We propose a platform for collecting task-oriented situated dialogue data by using gamification. Combining a video game with data collection benefits such as motivating workers and cost reduction. Our platform enables data collectors to create their original video game in which they can collect dialogue data of various types of tasks by using the logging function of the platform. Also, the platform provides the annotation function that enables players to annotate their own utterances. The annotation can be gamified aswell. We aim at high-quality annotation by introducing such self-annotation method. We implemented a prototype of the proposed platform and conducted a preliminary evaluation to obtain promising results in terms of both dialogue data collection and self-annotation.

Targeting the database search dialogue, we propose to utilise information in the user utterances that do not directly mention the database (DB) field of the backend database system but are useful for constructing database queries. We call this kind of information implicit conditions. Interpreting the implicit conditions enables the dialogue system more natural and efficient in communicating with humans. We formalised the interpretation of the implicit conditions as classifying user utterances into the related DB field while identifying the evidence for that classification at the same time. Introducing this new task is one of the contributions of this paper. We implemented two models for this task: an SVM-based model and an RCNN-based model. Through the evaluation using a corpus of simulated dialogues between a real estate agent and a customer, we found that the SVM-based model showed better performance than the RCNN-based model.

Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer.