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.

翻译：在对话系统的响应排序任务中，上下文信息对于捕捉多轮对话的主旨至关重要。本文提出一种灵活的神经框架，能够整合来自多个通道的上下文信息。具体针对当前任务，我们并行提供两个信息通道：融合对话历史与从候选响应来源中提取的领域知识（FCC），其中候选响应经过筛选，其上下文信息被用于提升多轮对话响应排序性能。所提出的方法可作为通用模块，为其他上下文导向任务整合各类杂项特征。我们在广泛用于对话响应排序评估的MSDialog数据集上验证模型，实验结果表明，该框架显著优于现有最先进模型，将Recall@1提升7%，MAP提升4%。此外，我们通过消融研究评估每个信息通道及框架组件对整体排序性能的贡献，为后续改进提供了额外见解与方向。