More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (2021)

Abstract: Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage. Thus, they often degenerate into traditional methods because not all dialogues can be linked with knowledge entries. This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: (1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time. Extensive evaluations on a Chinese dataset show the superior performance of this work against various state-of-the-art approaches. To our best knowledge, this work is the first to use the multi-source heterogeneous knowledge in the open-domain knowledge-enhanced dialogue generation.

## introduction the rapid developments of knowledge-enhanced techniques have enabled machines to understand the instinct semantics of human conversations further and generate informative responses @xcite . external knowledge, such as commonsense bases @xcite , documents @xcite , and tables @xcite , can bridge the gap between machines and humans in conversation by generously providing knowledge that is * corresponding author: ying li, li.ying@pku.edu.cn. the email of the first author: wusixing@pku.edu.cn commonsense (iphone, related to, smartphone) (smartphone, has context ,mobile phones) (android, has context, mobile phones) …. ## related work the vanilla seq2seq tends to generate generic responses, such as 'i don't know' @xcite . many efforts have been devoted to diversifying the generations @xcite , etc. one crucial factor leading to this issue is the lack of sufficient knowledge. during the conversation, the vanilla seq2seq model can only access the given query, which only contains limited knowledge @xcite . the insufficient knowledge makes it hard for a model to understand the context and generate an informative response. to this end, knowledge-enhanced approaches have been proposed and demonstrated promising performances @xcite . the knowledge can be texts @xcite @xcite , the structured graphs/tables/bases @xcite @xcite , the semi-structured infobox @xcite , the pre-trained models @xcite @xcite , and many other external knowledge components @xcite . however, most previous works can only use single-source homogeneous knowledge. solely re-lying on only one type of knowledge greatly limits the performance in the real scenario. some previous works have also noticed this issue. for example, augmenting the knowledge graph with an external text comprehension module @xcite or a kbqa module @xcite , introducing multi-modal visual features @xcite for emotional conversation or visual conversation @xcite . our work is different from them because we focus on the open-domain knowledge-enhanced dialogue response generation, rather than the emotional/visual conversation, etc. to our best knowledge, few works have studied this topic in this area. in addition, mske-dialog has a salable framework. a new knowledge source can easily be integrated by simply adding a knowledge encoder. ## conclusion & future work this paper proposes a novel multi-source heterogeneous knowledge-enhanced dialogue generation approach, mske-dialog, which outperforms competitive knowledge-enhanced baselines and pretraining models. it verifies the advantages of using multi-source heterogeneous knowledge and the advantages of our approach. we will continue to investigate the advantages of knowledge-enhanced dialogue generation. we notice the current decoding strategy may be a bottleneck of knowledge-enhanced works and the potential of multi-source knowledge + pre-training. we will also pay more attention to such topics.