KSAM : Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding (2022)

Abstract: Knowledge-enhanced methods have bridged the gap between human beings and machines in generating dialogue responses. However, most previous works solely seek knowledge from a single source, and thus they often fail to obtain available knowledge because of the insufficient coverage of a single knowledge source. To this end, infusing knowledge from multiple sources becomes a trend. This paper proposes a novel approach Knowledge Source Aware Multi-Head Decoding, KSAM, to infuse multi-source knowledge into dialogue generation more efficiently. Rather than following the traditional single decoder paradigm, KSAM uses multiple independent source-aware decoder heads to alleviate three challenging problems in infusing multi-source knowledge, namely, the diversity among different knowledge sources, the indefinite knowledge alignment issue, and the insufficient flexibility/scalability in knowledge usage. Experiments on a Chinese multi-source knowledge-aligned dataset demonstrate the superior performance of KSAM against various competitive approaches.

## introduction conversational ais play an indispensable role in the human-computer interaction @xcite . humans can use their learned knowledge to understand the context, reason the intrinsic semantic, and generate informative responses. however, traditional dialogue generation methods can only use dialogue history that carries limited knowledge to generate responses @xcite , bringing meaningless responses and frustrating user experience @xcite . to bridge such a gap, incorporating external knowledge into the dialogue generation is a feasible way @xcite . commonsense camera a camera is an optical ins trument that captures a visual image. lens h a s a dslr related to plain text table entity iphone 13 pro os ios 15 display 6.1-inch promotion … … dialogue 1 q: can you recommend a camera? r: i think dslr is a good choice. dialogue 2 q: apple just released the new iphone 13 pro. r: yep, promotion finally came to iphone! tba dialogue 3 q: do you know how to get bitcoin? r: i don't know what you're talking about. compared to the traditional non-knowledgeenhanced methods, the advantages of knowledgeenhanced methods come from the adopted external knowledge source @xcite . if a knowledge-enhanced model fails to seek available knowledge from the given knowledge source, it can only degenerate into a traditional manner. however, most previous works @xcite only seek knowledge from a single source. the knowledge coverage foot_0 of a single knowledge source is always insufficient @xcite ; thus, dialogues often can not benefit from the given knowledge source. meanwhile, a single knowledge source is also difficult to meet the various requirements in the real scenarios @xcite . recently, researchers began to seek knowledge from multiple sources to alleviate such issues. gokc generates dialogues conditioned on both the background knowledge and the goal knowledge @xcite ; the recent mske leverages heterogeneous knowledge from multiple sources @xcite . with more knowledge sources, they have successfully improved the performance of knowledge-enhanced dialogue generation. nonetheless, as illustrated in figure 1 , many challenges in infusing multi-source knowledge into dialogue generation have not been well solved: 1) knowledge diversity: notable differences inevitably appear among different types of knowledge sources, which can be attributed to the different structures (i.e., text knowledge @xcite vs. commonsense knowledge graph @xcite ), different domains (i.e., open-domain @xcite vs. specific-domain @xcite ), and many other factors @xcite . previous works only considered the difference in the encoding stage by using different knowledgespecific encoders, but failed to handle the difference in the decoding stage; 2) indefinite alignment: due to the limitation of knowledge coverage, a single dialogue usually can not fully use all n provided knowledge sources. depending on the situation, each case may use an arbitrary @xmath0, n] of sources, bringing more complexities; 3) insufficient flexibility and scalability: a model itself should not be limited to a knowledge combination of specific types and a specific amount. this paper proposes a ksam (knowledge source aware multi-head decoding) approach for multisource knowledge-enhanced dialogue generation, which explicitly considers the three challenges mentioned above. besides the dialogue history, ksam uses three different knowledge sources, i.e., plain text knowledge, commonsense fact knowledge, and table attribute knowledge, to generate the target response. we propose four source-specific encoders to encode such four input sources 2 . in the decoding stage, unlike previous works that only adopt a single-head decoder, we assign an independent source-aware decoder head for each input source. each decoder head is a source-aware and fully functional decoder network, generating a source-aware response independently. thus, we can handle the differences among multiple sources by tuning the source-specific encoder or the sourceaware decoder head without impacting other encoders/heads. subsequently, we propose a source fusion network (sfn) to make the final prediction by collecting and fusing the outputs from decoder heads. with source-aware decoder heads and the fusion gates outputted by sfn, ksam can alleviate the issue of indefinite knowledge-alignment. meanwhile, sfn does not limit the number of decoder heads or the type of a decoder head; thus, ksam theoretically supports the use of any combination of knowledge sources. we evaluate ksam and baseline models on a previously released chinese dataset @xcite , which is aligned to three knowledge sources, i.e., a plain text knowledge base, a commonsense knowledge base, and a table knowledge base. both the automatic and human evaluation results demonstrate the superior performance of ksam against various competitive baselines. we also conduct extensive experiments to analyze ksam further. ## conclusion & future work this paper studies the multi-source knowledgeenhanced dialogue generation. we find three challenging problems, i.e., 1) knowledge diversity, 2) indefinite alignment, and 3) insufficient flexibility and scalability. consequently, this paper proposes a novel knowledge source aware multi-head decoding approach, ksam, which employs multiple source-aware decoder heads to handle each knowledge source more efficiently. in the future, we will continue to improve the applicability and the performance of multi-source knowledge-enhanced dialogue generation. for example, improving the fusing the predictions of heads.