Persona-Coded Poly-Encoder: Persona-Guided Multi-Stream Conversational Sentence Scoring
This work addresses the problem of enhancing personalized conversational AI for users by integrating heterogeneous auxiliary data, though it appears incremental as it builds on existing Poly-Encoder methods.
The paper tackles the challenge of leveraging persona information to improve conversational AI quality by proposing a Persona-Coded Poly-Encoder method, which achieves improvements of 3.32% in BLEU score and 2.94% in HR@1 over a baseline.
Recent advances in machine learning and deep learning have led to the widespread use of Conversational AI in many practical applications. However, it is still very challenging to leverage auxiliary information that can provide conversational context or personalized tuning to improve the quality of conversations. For example, there has only been limited research on using an individuals persona information to improve conversation quality, and even state-of-the-art conversational AI techniques are unable to effectively leverage signals from heterogeneous sources of auxiliary data, such as multi-modal interaction data, demographics, SDOH data, etc. In this paper, we present a novel Persona-Coded Poly-Encoder method that leverages persona information in a multi-stream encoding scheme to improve the quality of response generation for conversations. To show the efficacy of the proposed method, we evaluate our method on two different persona-based conversational datasets, and compared against two state-of-the-art methods. Our experimental results and analysis demonstrate that our method can improve conversation quality over the baseline method Poly-Encoder by 3.32% and 2.94% in terms of BLEU score and HR@1, respectively. More significantly, our method offers a path to better utilization of multi-modal data in conversational tasks. Lastly, our study outlines several challenges and future research directions for advancing personalized conversational AI technology.