Towards Development of a Multilingual Mobile Chat Application for Enhanced Global Communication

The advent of mobile chat applications has revolutionized everyday communication. These applications facilitate the exchange of user's textual and multimedia content across languages and cultures. Most chat applications are known to only support a limited set of predominantly spoken languages, thereby, leaving a substantial portion of the user population without adequate multilingual support. This paper aims to bridge the linguistic gap by presenting Kobapp, a multilingual chat application. The Kobapp, leverages some of the cutting-edge technologies, such as React-Native, Next.js, and the DeepL API, to offer real-time, accurate translations while at the same time offering user privacy. The development process of the Kobapp is outlined from the system architecture and design, emphasizing the integration of a client-side (Android) and server-side using Node.js, Express.js, and MongoDB. Notably, user feedback plays a crucial role in shaping an application's features and functionality. Therefore, the application’s performance was evaluated through a conducted user study. Results of the study indicate a strong positive linear relationship between overall user satisfaction and translation accuracy for different language pairs. Moreover, the absence of outliers and the model's significance further reinforces the application's commitment to data quality and accuracy. Future research will explore new dimensions in multilingual communication and applications to promote a truly global community

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