Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation (2020)

Abstract: In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research. In this paper, we propose a difference-aware knowledge selection method. It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurately and generate more informative responses, significantly outperforming the state-of-the-art baselines.

## introduction knowledge-grounded conversation generation aims at generating informative responses based on both discourse context and external knowledge @xcite , where selecting appropriate knowledge is critical to the success of the task. existing knowledge selection models generally fall into two types. one type is solely based on the context @xcite @xcite , which we call non-sequential selection because knowledge selection at different turns is independent. the other type sequentially selects knowledge additionally conditioned on previously selected knowledge @xcite , which we figure 1 : an example of difference-aware knowledge selection. the blue denotes that the corresponding knowledge has little difference from or is identical to the previously selected one, and selecting it may lead to repetitive responses. the red × denotes that the difference is too large, and selecting it could make the response incoherent with the context. call sequential selection. as shown in @xcite , such a sequential way can better simulate a multi-turn dialog and facilitate knowledge selection in later turns. however, the difference between selected knowledge at different turns has been largely neglected in prior studies, while it usually provides potential clues to knowledge selection. figure 1 illustrates an example, where the dialog system selects one from candidate knowledge sentences (all relevant to the context) at the 2 nd turn. selecting the knowledge that has little difference from or even is identical to the previously selected one (like the 1 st knowledge) may lead to generating repetitive responses, while too large difference (like the 3 rd knowledge) would make the response incoherent with the context. as a result, the dialog system should avoid the knowledge which differs from the previously selected ones either too little or too largely, and instead select an appropriate knowledge sentence (the 2 nd one) which can make the conversation flow smoothly and naturally. we thus propose diffks, a novel differenceaware knowledge selection method for knowledgegrounded conversation generation. it first computes the difference between the candidate knowledge sentences provided at the current turn and the previously selected knowledge. then, in the two models we devise, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. automatic and human evaluation on two widely-used benchmarks shows that our method is significantly superior over the state-of-the-art baselines and it can select knowledge more accurately and generate more informative responses. our contributions are summarized as follows: ## conclusion we present a novel difference-aware knowledge selection method for multi-turn knowledge-grounded conversation generation. our method first compares the candidate knowledge provided at the current turn with the previously selected knowledge, and then selects appropriate knowledge to be used in generation. experimental results show that our method is able to select knowledge more accurately and to generate more informative responses, outperforming significantly the state-of-the-art baselines.