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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.
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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