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CommonAI: Quantitative and Qualitative Analysis for Automatic-generation of Commonsense Reasoning Conversations Suitable for AI
Hyeon Gyu Shin, Hyun Jo You, Young Sook Song
http://doi.org/10.5626/JOK.2023.50.5.407
Human-like common sense reasoning is now considered an essential component for improving the quality of natural language generation for chatbots and conversational agents, However, there is no clear consensus at present about to what extent AI systems require common sense. We discussed common sense requirements for AI chatbots based on quantitative and qualitative analysis of results from two experimental surveys to show differences between gender and age groups and variations according to conversation topics. The contribution of this paper is to refine preferences for chatbot conversations that provide useful information and show an appropriate level of empathy.
Method of Reflecting Various Personas in a Chatbot
Shinhyeok Oh, Seok-won Jung, Harksoo Kim
http://doi.org/10.5626/JOK.2021.48.2.160
A chatbot is a computer program that simulates human conversation. Research on generative chatbots that provide various responses based on personal characteristics has been increasing. Representatively, there are persona chatbots that reflect personal characteristics in chatbots. Persona chatbots refers to a chatbot that reflects persona, which means personal characteristics, and are gaining popularity due to the movement to reflect a brand personality in various services. In response to this trend, this paper proposes a chatbot model that can generate different responses for each persona by suggesting sentence persona encoder and table persona encoder that reflects personas based on dual WGAN generative chatbot. The performance of the proposed model is verified through comparative experiments and experimental examples for each module using quantitative and qualitative evaluation.
Usability Assessment of FHIR-based Geriatric Depression Scale Questionnaire Using Chatbot
http://doi.org/10.5626/JOK.2020.47.7.650
As Korea enters the aging society, the interest in, and importance of the elderly are increasing. In particular, the depression of the elderly is an important issue to be addressed. To this end, latency delays are among the most common complaints about those who seek medical examination or to see a doctor. Also, if patients move and are thus are sent to a different hospital because of a change of residence or personal reasons, they may undergo the same examination. In this case, fatigue and economic burden are placed upon the patient because of the re-examination. In this study, we have implemented the chatbot for the Geriatric Depression Scale Questionnaire based on the Fast Healthcare Interoperability Resource, an international health information exchange standard. Unlike the existing paper questionnaire, it has interoperable questionnaire information, and the user’s perceived usability was examined through the evaluation of usability.
Effective Generative Chatbot Model Trainable with a Small Dialogue Corpus
Jintae Kim, Hyeon-gu Lee, Harksoo Kim
http://doi.org/10.5626/JOK.2019.46.3.246
Contrary to popular retrieval-based chatbot models, generative chatbot models do not depend on predefined responses, but rather generate new responses based on well-trained neural networks. However, they require a large number of training corpus in the form of query-response pairs. If the training corpus are insufficient, they make grammatical errors emanating from out-of-vocabulary or sparse data problems, mostly in longer sentences. To overcome this challenge, we proposed a chatbot model based on sequence-to-sequence neural network using a mixture of words and syllables as encoding-decoding units. Moreover, we proposed a two-step training procedure involving pre-training using a large non-dialogue corpus and retraining using a smaller dialogue corpus. In the experiment involving small dialogue corpus (47,089 query-response pairs for training and 3,000 query-response pairs for evaluation), the proposed encoding-decoding units resulted to a reduction in out-of-vocabulary problem while the two-step training method led to improved performance measures like BLEU and ROUGE.
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