Generating Rational Commonsense Knowledge-Aware Dialogue Responses With Channel-Aware Knowledge Fusing Network

Abstract

Dialogues systems endow machines with the ability to converse with humans using natural language. Nonetheless, previous Seq2Seq-based generative dialogue systems often generate safe but meaningless responses, such as ‘I don't know' or ‘I think so'. To this end, researchers proposed to infuse external knowledge into dialogue generation, and such knowledge-enhanced methods have achieved remarkable improvements in the open-domain dialogue systems. External knowledge is an exogenous input, where the estrangement inevitably exists between knowledge and dialogue context. Although previous knowledge-enhanced works can already use commonsense knowledge to generate informative responses, they always use knowledge in a single-channel paradigm, which is hard to accurately handle different data-flows and then tends to generate irrational dialogue responses. Thus, they tend to be confused and generate strange responses when infusing the knowledge into dialogue generation, such as ‘I just ate a basketball’, dramatically degrading the user experience. To address this problem, this paper proposes a novel Channel-Aware Knowledge Fusing Network (CAKF). Rather than following the traditional single-channel paradigm, CAKF employs three unique channels to handle different data-flows more clearly and rationally: a base channel serves like a vanilla Seq2Seq decoder; a context channel to utilize the contextual information, and a knowledge channel to infuse commonsense knowledge into the dialogue generation. Above such three channels, a Sequential Manager is built to maintain the global sequential decision state, aggregate the local data-flows, and make the final prediction. Experiments on two open-released datasets (a Chinese Weibo and an English Reddit) demonstrated the superior performance of this work against various state-of-the-art approaches.