Existing conversational models are handled by a database(DB) and API based systems. However, very often users' questions require information that cannot be handled by such systems. Nonetheless, answers to these questions are available in the form of customer reviews and FAQs. DSTC-11 proposes a three stage pipeline consisting of knowledge seeking turn detection, knowledge selection and response generation to create a conversational model grounded on this subjective knowledge. In this paper, we focus on improving the knowledge selection module to enhance the overall system performance. In particular, we propose entity retrieval methods which result in an accurate and faster knowledge search. Our proposed Named Entity Recognition (NER) based entity retrieval method results in 7X faster search compared to the baseline model. Additionally, we also explore a potential keyword extraction method which can improve the accuracy of knowledge selection. Preliminary results show a 4 \% improvement in exact match score on knowledge selection task. The code is available https://github.com/raja-kumar/knowledge-grounded-TODS

翻译：现有对话模型通常基于数据库（DB）和API系统构建。然而，用户提出的问题常常涉及这些系统无法处理的信息。尽管如此，此类问题的答案仍可从客户评论和常见问题解答（FAQ）中获取。DSTC-11提出了一种三阶段流水线——包括知识导向轮次检测、知识选择与响应生成——用于构建基于主观知识的对话模型。本文聚焦于改进知识选择模块以提升整体系统性能。具体而言，我们提出了实体检索方法，能够实现更精确且更快速的知识搜索。相比基线模型，我们提出的基于命名实体识别（NER）的实体检索方法实现了7倍的搜索速度提升。此外，我们还探索了一种潜在的关键词提取方法，该方法可提高知识选择的准确率。初步结果表明，在知识选择任务上，精确匹配分数提升了4%。代码已开源：https://github.com/raja-kumar/knowledge-grounded-TODS