FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems
This work addresses the problem of improving retrieval-based conversational systems for users by enhancing contextual response ranking, though it is incremental as it builds on existing neural frameworks.
The paper tackles response ranking in multi-turn dialogues by fusing conversation history and candidate provenance as contextual information, achieving a 7% improvement in Recall@1 and 4% in MAP over previous state-of-the-art models on the MSDialog dataset.
Response ranking in dialogues plays a crucial role in retrieval-based conversational systems. In a multi-turn dialogue, to capture the gist of a conversation, contextual information serves as essential knowledge to achieve this goal. In this paper, we present a flexible neural framework that can integrate contextual information from multiple channels. Specifically for the current task, our approach is to provide two information channels in parallel, Fusing Conversation history and domain knowledge extracted from Candidate provenance (FCC), where candidate responses are curated, as contextual information to improve the performance of multi-turn dialogue response ranking. The proposed approach can be generalized as a module to incorporate miscellaneous contextual features for other context-oriented tasks. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks. Our experimental results show that our framework significantly outperforms the previous state-of-the-art models, improving Recall@1 by 7% and MAP by 4%. Furthermore, we conduct ablation studies to evaluate the contributions of each information channel, and of the framework components, to the overall ranking performance, providing additional insights and directions for further improvements.