Positively transitioned sentiment dialogue corpus for developing emotion-affective open-domain chatbots

TL;DR

This paper introduces a new dialogue corpus with positively transitioned sentiment data to enhance emotion-aware chatbots, demonstrating improved performance over existing models.

Contribution

It presents a novel data enhancement method using positively transitioned sentiment data and releases a new corpus for developing more emotionally intelligent chatbots.

Findings

Emily outperforms state-of-the-art chatbots in emotion-related metrics

The PT-enhanced training improves chatbot emotional responsiveness

Publicly released corpus supports further research

Abstract

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.