Generative Knowledge Selection for Knowledge-Grounded Dialogues (2023)

Abstract: Knowledge selection is the key in knowledge-grounded dialogues (KGD), which aims to select an appropriate knowledge snippet to be used in the utterance based on dialogue history. Previous studies mainly employ the classification approach to classify each candidate snippet as “relevant” or “irrelevant” independently. However, such approaches neglect the interactions between snippets, leading to difficulties in inferring the meaning of snippets. Moreover, they lack modeling of the discourse structure of dialogue-knowledge interactions. We propose a simple yet effective generative approach for knowledge selection, called GenKS. GenKS learns to select snippets by generating their identifiers with a sequence-to-sequence model. GenKS therefore captures intra-knowledge interaction inherently through attention mechanisms. Meanwhile, we devise a hyperlink mechanism to model the dialogue-knowledge interactions explicitly. We conduct experiments on three benchmark datasets, and verify GenKS achieves the best results on both knowledge selection and response generation.

## introduction to improve the informativeness in open-domain dialogue agents @xcite , knowledgegrounded dialogues (kgd) are proposed to leverage external structured @xcite and unstructured @xcite knowledge to dialogue responses. in kgd, it is pivotal to embed factual and conversationally appropriate knowledge in responses. two classes of approaches are considered to embed knowledge: end-to-end and pipeline. end-to-end models, such as fid @xcite , process the document and generate the response in one shot. however, they tend to misuse knowledge @xcite . pipeline models address this problem by explicitly identifying a specific knowledge snippet to be used in the response @xcite . typically, pipeline kgd approaches have two substeps, i.e., knowledge selection and response generation @xcite : the former aims to select knowledge snippets from passages, and the latter generates responses based on them. knowledge selection plays a vital role in kgd as it directly determines the content of the response @xcite . in this paper, we focus on selecting knowledge snippets for dialogue to enhance pipeline kgd models. the classification paradigm dominates knowledge selection studies. in this paradigm, each snippet is independently classified as "relevant" or "irrelevant" @xcite . however, these approaches ignore knowledge interactions, which refer to flows of information within the knowledge or between knowledge and dialogues. as shown in figure 1 , we identify two types of knowledge interactions in kgd: intra-knowledge interaction intra-knowledge interaction refers to the interactions between snippets. it is worth noting that the meaning of a knowledge snippet is context-dependent and can be ambiguous when taken individually. for example, @xmath0, and is difficult to identify its meaning without knowing the remaining context of the sentence. however, with the existence of the remaining context, we can quickly infer that it refers to lamarr and george antheil. this problem challenges existing methods when selecting knowledge on new topics. ## related work knowledge-grounded dialogues with the advances in large-scale language models, dialogue agents can now generate high-quality responses using parametric knowledge @xcite @xcite . however, hallucination remains a challenge, which means that the language model tends to generate plausible-looking statements that are factually incorrect @xcite . to address this problem, knowledge-augmented approaches are applied in dialogue generation @xcite . in knowledge-grounded dialogues (kgd), the dialogue models first select a knowledge snippet from passages and then generate the responses @xcite . knowledge selection as the critical step in kgd, knowledge selection has received many studies. the exiting methods mainly employ classification model with dual-encoder @xcite or cross-encoder @xcite architecture. however, the classification paradigm is unable to capture the knowledge interaction in kgd @xcite . to address this problem, @xcite propose a graph-based method to capture the relationship between candidate snippets, @xcite and @xcite employ machine reading comprehension model to extract span from long document. sequential knowledge selection has also been proposed to capture the topic transition in conversations @xcite @xcite @xcite . despite their effectiveness, the existing methods have two drawbacks: (1) they use compact vectors to represent dialogue and knowledge and thus lack deep semantic interactions; (2) they are complicated and challenging to capture intra-knowledge and dialogue-knowledge interactions simultaneously. we address these drawbacks by shifting the modeling paradigm of knowledge selection to identifier generation @xcite , and propose genks to capture the two types of interaction simultaneously using transformer @xcite . generative knowledge selection a generative paradigm for knowledge selection is not foreign to the nlp community; for example, sequenceto-sequence models have been applied on entity retrieval @xcite , document ranking @xcite , multievidence retrieval @xcite , and etc. our proposed model genks differs from existing methods in the following ways: ## genks we provide an overview of genks in figure 2 . as shown in figure 2 , the dialogue data is first serialized into a sequence. then a sequence-to-sequence model (i.e., bart) is employed to select knowl-edge and get the response by generating the target sequence autoregressively. in this section, we first formulate the task in section 3.1. then, we detail the serialization (section 3.2) and optimization (section 3.3) methods. ## conclusion in this paper, we have proposed genks, a simple yet effective knowledge-grounded dialogue model. genks is a generative model, which learns to select knowledge snippets by generating their identifiers. benefiting from the modeling of intraknowledge interaction and dialogue-knowledge interaction, genks effectively addresses the challenges of ambiguity and discourse structure. our experiments have shown that genks establishes a new state-of-the-art on three knowledge-grounded dialogue benchmarks. notably, genks particularly excels at new topics and as the dialogue goes deeper. genks also outperforms sota end-toend models. hence, we believe genks reveals a new paradigm for knowledge selection in opendomain dialogue.