论文标题
您现在想谈论运动吗？开放域对话代理的上下文主题建议
Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents
论文作者
论文摘要
为了进行真实的对话，智能代理应该能够偶尔采取主动权并推荐下一个自然对话主题。这是一项具有挑战性的任务。代理商建议的主题应与该人相关，适合对话上下文，而代理人应该对此有一些有趣的话要说。因此，脚本化或一定程度的基于普及的主题建议注定要失败。取而代之的是，我们探索了一个个性化的，上下文主题建议的开放域对话建议。我们正式化对话主题建议问题（CTS），以更清楚地确定假设和要求。我们还探索了解决此问题的三种可能的方法：（1）基于模型的顺序主题建议，以捕获对话上下文（CTS-SEQ），（2）基于协作过滤的建议，以捕获相似用户（CTS-CF）的先前成功对话，以及（3）混合方法结合了对话上下文和协作过滤。为了评估这些方法的有效性，我们将收集的实际对话作为Amazon Alexa奖2018对话AI挑战的一部分。结果很有希望：CTS-SEQ模型提出的主题比基线高23％，并将协作过滤信号纳入混合CTS-SEQ-CF模型，进一步提高了建议准确性12％。我们提出的模型，实验和分析共同研究了开放域对话剂的研究，并为未来改进的有希望的方向提出了有希望的方向。
To hold a true conversation, an intelligent agent should be able to occasionally take initiative and recommend the next natural conversation topic. This is a challenging task. A topic suggested by the agent should be relevant to the person, appropriate for the conversation context, and the agent should have something interesting to say about it. Thus, a scripted, or one-size-fits-all, popularity-based topic suggestion is doomed to fail. Instead, we explore different methods for a personalized, contextual topic suggestion for open-domain conversations. We formalize the Conversational Topic Suggestion problem (CTS) to more clearly identify the assumptions and requirements. We also explore three possible approaches to solve this problem: (1) model-based sequential topic suggestion to capture the conversation context (CTS-Seq), (2) Collaborative Filtering-based suggestion to capture previous successful conversations from similar users (CTS-CF), and (3) a hybrid approach combining both conversation context and collaborative filtering. To evaluate the effectiveness of these methods, we use real conversations collected as part of the Amazon Alexa Prize 2018 Conversational AI challenge. The results are promising: the CTS-Seq model suggests topics with 23% higher accuracy than the baseline, and incorporating collaborative filtering signals into a hybrid CTS-Seq-CF model further improves recommendation accuracy by 12%. Together, our proposed models, experiments, and analysis significantly advance the study of open-domain conversational agents, and suggest promising directions for future improvements.
