协调代码混合对话：对话中人格辅助的代码混合回复生成
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues
AI总结：
针对印地语—英语代码混合对话，提出利用无监督 Big Five 人格特质并通过 PA3 两步注意力融合机制增强回复生成，实验表明其 ROUGE 与 BLUE 表现优于传统模型。
摘要：
代码混合是指在单个对话中融合多种语言，这带来了一项独特挑战，尤其是在回复生成场景中。鉴于个体说话风格和文化背景会造成广泛差异，捕捉代码混合的复杂细节被证明是一项艰巨任务。本研究探讨代码混合对话中的回复生成。我们提出一种新颖方法，核心是利用以无监督方式从对话中获得的 Big Five 人格特质，以提升回复生成性能。这些推断出的人格属性通过一种新型融合机制 PA3 被无缝编织到对话语境结构中。该机制采用有效的两步注意力公式来融合对话和人格信息。这种融合不仅增强了所生成回复的语境相关性，还提升了模型的整体性能。我们的实验结果基于一个包含多方印地语—英语代码混合对话的数据集，凸显了注入人格的模型相比传统模型所具有的显著优势。当识别出的人格被无缝整合进对话语境时，回复生成任务的 ROUGE 和 BLUE 分数均有提升，这一点清楚地证明了上述优势。人格识别与回复生成的定性评估也与我们的定量结果高度一致。
英文摘要：
Code-mixing, the blending of multiple languages within a single conversation, introduces a distinctive challenge, particularly in the context of response generation. Capturing the intricacies of code-mixing proves to be a formidable task, given the wide-ranging variations influenced by individual speaking styles and cultural backgrounds. In this study, we explore response generation within code-mixed conversations. We introduce a novel approach centered on harnessing the Big Five personality traits acquired in an unsupervised manner from the conversations to bolster the performance of response generation. These inferred personality attributes are seamlessly woven into the fabric of the dialogue context, using a novel fusion mechanism, PA3. It uses an effective two-step attention formulation to fuse the dialogue and personality information. This fusion not only enhances the contextual relevance of generated responses but also elevates the overall performance of the model. Our experimental results, grounded in a dataset comprising of multi-party Hindi-English code-mix conversations, highlight the substantial advantages offered by personality-infused models over their conventional counterparts. This is evident in the increase observed in ROUGE and BLUE scores for the response generation task when the identified personality is seamlessly integrated into the dialogue context. Qualitative assessment for personality identification and response generation aligns well with our quantitative results.
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。
