FCC：融合对话历史与候选来源的上下文响应排序
FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems
AI总结：
本文提出FCC框架，通过融合对话历史与候选来源的领域知识提升多轮对话响应排序性能，在MSDialog数据集上Recall@1提升7%、MAP提升4%。
摘要：
对话中的响应排序在基于检索的会话系统中起着至关重要的作用。在多轮对话中，为了捕捉对话的要点，上下文信息作为实现这一目标的关键知识。本文提出了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，我们的方法是并行提供两个信息通道，即融合对话历史与从候选来源提取的领域知识（FCC），其中候选响应被精心整理作为上下文信息，以提升多轮对话响应排序的性能。所提出的方法可泛化为一个模块，用于为其他面向上下文的任务整合多种上下文特征。我们在广泛用于评估会话响应排序任务的MSDialog数据集上评估了我们的模型。实验结果表明，我们的框架显著优于先前的最先进模型，将Recall@1提升了7%，MAP提升了4%。此外，我们进行了消融研究，以评估每个信息通道及框架组件对整体排序性能的贡献，为进一步改进提供了额外的见解和方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
