Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues (2401.09248v2)

Abstract: Implicit user feedback, user emotions and demographic information have shown to be promising sources for improving the accuracy and user engagement of responses generated by dialogue systems. However, the influence of such information on task completion and factual consistency, which are important criteria for task-oriented and document-grounded dialogues, is not yet known. To address this, we introduce FEDI, the first English task-oriented and document-grounded dialogue dataset annotated with this information. Our experiments with Flan-T5, GPT-2 and Llama 2 show a particularly positive impact on task completion and factual consistency. Participants in our human evaluation reported that the responses generated by the feedback-trained models were more informative (Flan-T5 and GPT-2), relevant and factual consistent (Llama 2).

• The paper demonstrates that combining implicit user feedback, emotions, and demographic data significantly improves dialogue system performance.
• It employs advanced language models like FLAN-T5, GPT-2, and LLaMA-2 to validate enhanced task completion and factual coherence.
• A mixed methodology using synthetic generation and human evaluations underpins the dataset, paving the way for future multimodal dialogue research.

Introduction to FEDI

The development of dialogue systems often focuses on task completion and factual accuracy. However, success in human-computer interaction significantly hinges on user acceptance and enjoyment. A novel dataset named FEDI (Feedback, Emotions, and Demographic Information) offers a groundbreaking approach to enriching task-oriented document-grounded dialogue systems. By integrating implicit user feedback with emotional and demographic layers, FEDI aims to catalyze improvements in user engagement and the reliability of system responses.

Innovation in Dialogue Systems

Current natural language processing research tends to isolate demographic data, emotional states, and implicit feedback when training dialogue systems, which overlooks the interconnectedness of these aspects. FEDI, however, brings these elements into a cohesive analytical model. It is a pioneering English dialogue dataset that marks the first attempt in the field to incorporate this trichotomy for task-oriented dialogue advancement. Its structure consists of 8,000 dialogues, with 6,000 carrying annotations for implicit user feedback. Using powerful LLMs like FLAN-T5, GPT-2, and LLaMA-2, the paper showcases how this triad of information can significantly enhance task resolution and the factual coherence of generated dialogues.

FEDI's Dataset Creation

The inventiveness of FEDI extends to its generation process, utilizing the GPT-3.5-Turbo model to efficiently create high-quality training and validation dialogue data. To supplement this and address the limitations of synthetic data, human evaluators were employed in quality assessment and in compiling a separate set of test dialogues. This mixed methodology ensures the robustness of the dataset. FEDI also stands out by incorporating feedback scenarios relevant to a variety of contexts, as opposed to the typically narrow focus seen in preceding works.

Insights and Future Directions

Through rigorous experimentation, the paper vindicates that combining demographic information, user emotions, and implicit feedback can indeed fortify task completions and the quality of generated responses. Further, human evaluations affirm a palpable improvement in the systems' capability to engage users, a testament to the potential of multi-faceted, human-centric datasets like FEDI. Beyond its immediate applications, the paper sets the stage for subsequent explorations, contemplating enhancements in FEDI's annotation quality and a broader generalization across tasks and domains. It even proposes expanding into multimodal territory, integrating visual and audio signals to further the dialogue system's empathy and intuitive responses.

The incorporation of complex human aspects such as emotions and implicit signals into dialogue systems marks a remarkable step forward. As reflected by FEDI's promising start, future dialogue systems that understand not just the words but the feelings and backgrounds of their users can transform interactions from merely transactional to genuinely interactive experiences.