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.

该研究探讨了多语言混合对话中的回应生成问题，并介绍了一种利用从对话中无监督获取的大五人格特质来提高回应生成性能的新方法。实验结果表明，将人格融入对话背景可以显著增强生成回应的上下文相关性，并提高模型的整体性能。