In this paper, we describe a data enhancement method for developing Emily, an emotion-affective open-domain chatbot. The proposed method is based on explicitly modeling positively transitioned (PT) sentiment data from multi-turn dialogues. We construct a dialogue corpus with PT sentiment data and will release it for public use. By fine-tuning a pretrained dialogue model using the produced PT-enhanced dialogues, we are able to develop an emotion-affective open-domain chatbot exhibiting close-to-human performance in various emotion-affective metrics. We evaluate Emily against a few state-of-the-art (SOTA) open-domain chatbots and show the effectiveness of the proposed approach. The corpus is made publicly available.
@article{arxiv.2208.04565,
title = {Positively transitioned sentiment dialogue corpus for developing emotion-affective open-domain chatbots},
author = {Weixuan Wang and Wei Peng and Chong Hsuan Huang and Haoran Wang},
journal= {arXiv preprint arXiv:2208.04565},
year = {2022}
}
Comments
This paper drills down to the details not covered in its system paper arXiv:2109.08875, which has a broader scope