Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation

Abstract

Previous neural models on open-domain conversation generation have no effective mechanisms to manage chatting topics, and tend to produce less coherent dialogs. Inspired by the strategies in human-human dialogs, we divide the task of multi-turn open-domain conversation generation into two sub-tasks: explicit goal (chatting about a topic) sequence planning and goal completion by topic elaboration. To this end, we propose a three-layer Knowledge aware Hierarchical Reinforcement Learning based Model (KnowHRL). Specifically, for the first sub-task, the upper-layer policy learns to traverse a knowledge graph (KG) in order to plan a high-level goal sequence towards a good balance between dialog coherence and topic consistency with user interests. For the second sub-task, the middle-layer policy and the lower-layer one work together to produce an in-depth multi-turn conversation about a single topic with a goal-driven generation mechanism. The capability of goal-sequence planning enables chatbots to conduct proactive open-domain conversations towards recommended topics, which has many practical applications. Experiments demonstrate that our model outperforms state of the art baselines in terms of user-interest consistency, dialog coherence, and knowledge accuracy.

Towards Conversational Recommendation over Multi-Type Dialogs

“…However, these models can just produce a dialog towards a single goal, instead of a goal sequence as done in this work. We notice that the model by Xu et al (2020) Figure 2: We collect multiple sequential dialogs {d s k i } for each seeker s k . For annotation of every dialog, the recommender makes personalized recommendations according to task templates, knowledge graph and the seeker profile built so far.…”

“…To address this task, inspired by the work of Xu et al (2020), we present a multi-goal driven conversation generation framework (MGCG) to handle multi-type dialogs simultaneously, such as QA/chitchat/recommendation/task etc.. It consists of a goal planning module and a goal-guided responding module.…”

Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation

“…In this work, we investigate how to use prior dialog-transition information to facilitate dialog policy learning. Knowledge aware conversation generation There are growing interests in leveraging knowledge bases for generation of more informative responses (Dinan et al, 2019;Ghazvininejad et al, 2018;Moghe et al, 2018;Zhou et al, 2018;Liu et al, 2019;Bao et al, 2019;Xu et al, 2020). In this work, we employ a dialog-modeling oriented graph built from dialog corpora, instead of a external knowledge base, in order to facilitate multi-turn policy learning, instead of dialog informativeness improvement.…”

Information Seeking in the Spirit of Learning: A Dataset for Conversational Curiosity

“…Qin et al (2019) use Reddit discussion threads as conversations and ground to web pages. Similarly, Ghazvininejad et al (2018) collect Twitter threeturn threads and ground to Foursquare restaurant reviews. Our work adds to this dataset compendium.…”