PEDM: A Multi-task Learning Model for Persona-aware Emoji-embedded Dialogue Generation
Sirui Zhao, Hongyu Jiang, Hanqing Tao, Rui Zha, Kun Zhang, Tong Xu, Enhong Chen
Abstract
As a vivid and linguistic symbol, Emojis have become a prevailing medium interspersed in text-based communication (e.g., social media and chit-chat) to express emotions, attitudes, and situations. Generally speaking, a social-oriented chatbot that can generate appropriate Emoji-embedded responses would be much more competitive, making communications more fun, engaging, and human-like. However, the current Emoji-related research is still in its infancy, leading to an awkward situation of data deficiency. How to develop an Emoji-embedded dialogue system while addressing the lack of data will be interesting and meaningful for the application of future AI. To bridge this gap, we propose a multi-task learning method for persona-aware Emoji-embedded dialogue generation in this article. Specifically, as the benchmark of model training and evaluation, which includes 1.2 million Emoji-embedded tweets and 1.1 million post-response pairs, we first construct a dataset named EmojiTweet to handle the data deficiency problem. Then, a Seq2Seq-based model with multi-task learning is designed to simultaneously learn response generation and Emoji embedding from the constructed non-Emoji dialogue and Emoji-embedded monologue data. Afterward, we incorporate persona factors into our model by adopting persona fusion and personalized bias methods to deliver personalized dialogues with more accurately selected Emojis. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our model has three key benefits: improved dialogue quality, higher user engagement, and not relying on large-scale Emoji-embedded dialogue data representing specific personas. EmojiTweet will be published publicly via https://mea-lab-421.github.io/EmojiTweet/ .