论文标题
通过多类对话进行对话建议
Towards Conversational Recommendation over Multi-Type Dialogs
论文作者
论文摘要
我们提出了一项关于多类对话的对话性建议的新任务，在该对话框中可以考虑用户的兴趣和反馈，可以主动自然地将对话从非申请对话框（例如QA）引向推荐对话框。为了促进该任务的研究，我们创建了一个人对人类的中文对话框数据集\ emph {durecdial}（大约是10k对话框，156K话语），其中包含每对推荐寻求者（用户）（用户）和推荐人（Bot）的多个顺序对话框。在每个对话框中，推荐人会主动领导一个多类型对话框，以实现建议目标，然后以丰富的相互作用行为提出多个建议。该数据集允许我们系统地研究整体问题的不同部分，例如，如何自然地领导对话框，如何与用户进行互动以获取建议。最后，我们为未来的研究建立了基线结果。数据集和代码可在https://github.com/paddlepaddle/models/tree/develop/paddlenlp/research/acl2020-durecdial上公开获得。
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset \emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies. Dataset and codes are publicly available at https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial.
