Augmenting Topic Aware Knowledge-Grounded Conversations with Dynamic Built Knowledge Graphs (2021)

Abstract: Dialog topic management and background knowledge selection are essential factors for the success of knowledge-grounded open-domain conversations. However, existing models are primarily performed with symmetric knowledge bases or stylized with pre-defined roles between conversational partners, while people usually have their own knowledge before a real chit-chat. To address this problem, we propose a dynamic knowledge graph-based topical conversation model (DKGT). Given a dialog history context, our model first builds knowledge graphs from the context as an imitation of human’s ability to form logical relationships between known and unknown topics during a conversation. This logical information will be fed into a topic predictor to promote topic management, then facilitate background knowledge selection and response generation. To the best of our knowledge, this is the first attempt to dynamically form knowledge graphs between chatting topics to assist dialog topic management during a conversation. Experimental results manifest that our model can properly schedule conversational topics and pick suitable knowledge to generate informative responses comparing to several strong baselines.

## introduction conversational ai, especially the open-domain dialog system, is an essential and challenging problem that leads to a variety of applications @xcite . previous works introduce external background knowledge to help their systems generate more informative responses @xcite @xcite . however, these systems are facing a main issue that they tend to only utilize dialog utterances as queries to match appropriate knowledge sentences. to solve this problem, some papers propose to plan a set of conversational topics as chatting goals in advance to boost knowledge matching and response generation @xcite . however, it is difficult to schedule an appropriate topic transition route beforehand since topics are switching dynamically during a chit-chat based on many real-time factors, especially when two partners have different personal knowledge. hence, these methods could not pre-schedule a topic at each turn properly and thus becoming nonattractive. another problem these knowledge-grounded or topic-enhanced models might encounter is that they are typically tested under symmetric knowledge settings @xcite , or asymmetric settings with pre-defined roles @xcite . yet people usually have unequal personal knowledge prior to real-world conversations. hence, such models cannot reflect the effect of information transferring and learning between two strangers, which is crucial to an engaging conversation. this issue will matter more when there are no pre-defined roles between two conversation partners. to solve these problems, in this paper, we study the problem of topic transitions in open-domain conversations under both symmetric and asymmetric settings. to this end, we propose a dynamic knowledge graph-based topical conversation model (dkgt). given a dialogue context and a corresponding knowledge base, we first extract knowledge triples from each utterance and then jointly combine those triples through a static graph attention mechanism. such logical information will then be fed into a topic predictor to predict the next chatting topic, which assists background knowledge selection and dialog generation. we further demonstrate the effectiveness of our method on topical-chat @xcite , comparing to several baselines. the main contributions of this paper can be wrapped as follows: topic-aware conversational models a variety of approaches proposed to leverage topic information by recognizing topic words inside or outside the previous utterances @xcite @xcite . however, simply fusing topic words into text representations makes these models ignore logical relationships between topics and thus fail to perform smooth topic transitions. except for applying attention mechanism on topic words, researchers have also investigated proactive conversation, whose topic transition and conversation development are conditioned on pre-scheduled chatting goals @xcite @xcite . nevertheless, these models are limited by pre-defined topic and goal sequences, hence not applicable to open-domain and open-topic conversations. graph-enhanced conversational models structured knowledge has been studied to improve dialog generation for a long time @xcite @xcite . however, these models are mainly based on pre-defined knowledge graphs, which restrict their ability under symmetric knowledge settings. by contrast, our dynamic knowledge graphs enable our system to learn logical information through the conversation like humans, which facilitates both topic management and dialog generation. ## conclusion and future work in this paper, we propose a dynamic knowledge graph-based topical conversation model (dkgt) to perform coherent topic transitions under both symmetric and asymmetric knowledge settings. specifically, a dynamic graph builder that constructs knowledge graphs from the context to form logical relationships between known and unknown topics is introduced to assist topic management. automatic and manual evaluation results show that dkgt can not only schedule dialog topics properly but also generate informative responses preferred by humans. in the future, we will further explore the usage of our proposed dynamic knowledge graph strategy to improve chatbot's interpretability, which may depend on some inferential methods like multi-hop reasoning. we release our codes at https: //github.com/wujunjie1998/dkgt .