Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks

Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model

Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue

We present the Conversation Analysis Modeling Schema (CAMS), a novel dialogue labeling schema that combines the Conversation Analysis concept of Adjacency Pairs, with Dialogue Acts. The aim is to capture both the semantic and syntactic structure of dialogue, in a format that is independent of the domain or topic, and which facilitates the computational modeling of dialogue. A labeling task undertaken by novice annotators is used to evaluate its efficacy on a selection of task-oriented and non-task-oriented dialogs, and to measure inter-annotator agreement. To deepen the “human-factors” analysis we also record and examine users’ self-reported confidence scores and average utterance annotation times. Inter-annotator agreement is shown to be higher for task-oriented dialogs than non-task-oriented, though the structure of the dialogue itself has a more significant impact. We further examine the assumptions around expected agreement for two weighted agreement coefficients, Alpha and Beta, and show that annotators assign labels using similar probability distributions, small variations can result in large differences in agreement values between biased and unbiased measures