SARCAT : Generative Span-Act Guided Response Generation using Copy-enhanced Target Augmentation (2024)

Abstract: In this paper, we present a novel extension to improve the document grounded response generation, by proposing the Generative Span Act Guided Response Generation using Copy enhanced Target Augmentation (SARCAT) that consists of two major components as follows: 1) Copy-enhanced target-side input augmentation is an extended data augmentation to deal with the exposure bias problem by additionally incorporating the copy mechanism on top of the target-side augmentation (Xie et al., 2021). 2) Span-act guided response generation, which first predicts grounding spans and dialogue acts before generating a response. Experiment results on validation set in MultiDoc2Dial show that the proposed SARSAT leads to improvement over strong baselines on both seen and unseen settings and achieves the start-of the-art performance, even with the base reader using the pretrained T5-base model.

## introduction recently, there has been a surge in research interest in developing dialogue systems grounded in knowledge and multiple documents @xcite @xcite . existing approaches for document-and knowledge-grounded dialogue systems are based on the open-book approach, which is typically based on the retrieve-and-generate framework @xcite @xcite , or the close-book approach, which relies on the scalability of pretrained language models. this paper addresses the generation step in the open-book approach, i.e., document-grounded response generation, where the goal is to generate an appropriate response given a conversational history and retrieved contents. ## related work knowledge selection is the basic component guiding response generation in the existing documentand knowledge-grounded dialogue generation methods, including the long short-term memory (lstm)-based sequential knowledge selector @xcite , he sequential latent variable model @xcite , and retrievalbased knowledge selection on an external database @xcite . in multidoc2dial @xcite , e dense retrieval is typically performed as an initial step for knowledge selection under the retriever-reader framework @xcite @xcite , as in rag @xcite , which is the official baseline method. in particular, @xcite further elaborated the method of guiding the response generation by relying not only on a set of retrieved passages, but also on the predicted spans which are much shorter than passages although @xcite ) used a separate model for span prediction, their method employed a unified single t5 model to predict both spans and responses, similar to a strand of the chain-ofthought @xcite . unlike @xcite ) that directly predicts span tokens, our model instead predicts span markers with positional information, as we are largely motivated by the recent "marker"-based extensions of fid @xcite , such as fid-ex @xcite and pathfid @xcite . ## methods this section presents the details of the proposed components. figure 1 illustrates an overall architecture of the proposed sarcat framework, which consists of two components: copy-enhanced tia and span-act guided generation. ## conclusion in this paper, we presented sarcat as novel fremework for improving document-grounded response generation. sarcat consists of cat, which explicitly incorporates a copy mechanism into tia, enabling the infusion of context knowledge that is likely to be copied, and sar, which initially generates a span-act sequence as an intermediate reasoning step to effectively guide the response generation process. experimental results obtained using the multidoc2dial dataset showed that the proposed framework outperformed the baseline run and achieved state-of-the-art performance.