Adding SPICE to Life: Speaker Profiling in Multiparty Conversations
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Abstract
In the realm of conversational dynamics, individual idiosyncrasies challenge the suitability of a one-size-fits-all approach for dialogue agent responses. Prior studies often assumed the speaker’s persona’s immediate availability, a premise not universally applicable. To address this gap, we explore the Speaker Profiling in Conversations (SPC) task, aiming to synthesize persona attributes for each dialogue participant. SPC comprises three core subtasks: persona discovery, persona-type identification, and persona-value extraction. The first subtask identifies persona-related utterances, the second classifies specific attributes, and the third extracts precise values for the persona. To confront this multifaceted challenge, we’ve diligently compiled SPICE, an annotated dataset, underpinning our thorough evaluation of diverse baseline models. Additionally, we benchmark these findings against our innovative neural model, SPOT, presenting an exhaustive analysis encompassing a nuanced assessment of quantitative and qualitative merits and limitations.