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.

Utilising effective features in machine learning-based natural language processing (NLP) is crucial in achieving good performance for a given NLP task. The paper describes a pilot study on the analysis of eye-tracking data during named entity (NE) annotation, aiming at obtaining insights into effective features for the NE recognition task. The eye gaze data were collected from 10 annotators and analysed regarding working time and fixation distribution. The results of the preliminary qualitative analysis showed that human annotators tend to look at broader contexts around the target NE than recent state-of-the-art automatic NE recognition systems and to use predicate argument relations to identify the NE categories.

In this paper, we propose utilising eye gaze information for estimating parameters of a Japanese predicate argument structure (PAS) analysis model. We employ not only linguistic information in the text, but also the information of annotator eye gaze during their annotation process. We hypothesise that annotator’s frequent looks at certain candidates imply their plausibility of being the argument of the predicate. Based on this hypothesis, we consider annotator eye gaze for estimating the model parameters of the PAS analysis. The evaluation experiment showed that introducing eye gaze information increased the accuracy of the PAS analysis by 0.05 compared with the conventional methods.

In this paper, we extend an existing annotation scheme ISO-Space for annotating necessary spatial information for the task placing an specified object at a specified location with a specified direction according to a natural language instruction. We call such task the spatial placement problem. Our extension particularly focuses on describing the object direction, when the object is placed on the 2D plane. We conducted an annotation experiment in which a corpus of 20 situated dialogues were annotated. The annotation result showed the number of newly introduced tags by our proposal is not negligible. We also implemented an analyser that automatically assigns the proposed tags to the corpus and evaluated its performance. The result showed that the performance for entity tag was quite high ranging from 0.68 to 0.99 in F-measure, but not the case for relation tags, i.e. less than 0.4 in F-measure.