Understanding and Improving the Exemplar-based Generation for Open-domain Conversation (2022)

Abstract: Exemplar-based generative models for open-domain conversation produce responses based on the exemplars provided by the retriever, taking advantage of generative models and retrieval models. However, due to the one-to-many problem of the open-domain conversation, they often ignore the retrieved exemplars while generating responses or produce responses over-fitted to the retrieved exemplars. To address these advantages, we introduce a training method selecting exemplars that are semantically relevant to the gold response but lexically distanced from the gold response. In the training phase, our training method first uses the gold response instead of dialogue context as a query to select exemplars that are semantically relevant to the gold response. And then, it eliminates the exemplars that lexically resemble the gold responses to alleviate the dependency of the generative models on that exemplars. The remaining exemplars could be irrelevant to the given context since they are searched depending on the gold response. Thus, our training method further utilizes the relevance scores between the given context and the exemplars to penalize the irrelevant exemplars. Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.

## introduction exemplar-based generative models @xcite @xcite for open-domain conversation combine a retrieval model @xcite @xcite and a generative model (adiwardana et al., 2020; roller et al., 2021; ## method we hypothesize that selecting semantically relevant but lexically distanced exemplars from the gold response could solve the drawbacks above. to validate this hypothesis, we introduce a training method of exemplar-based generative models, called corge. our proposed training method is illustrated in figure 3 , and the illustrative examples about the exemplars selected by corge are described in ## conclusion in this paper, we introduce a generally applicable training method for exemplar-based generative models to alleviate their disadvantages derived from the one-to-many problem. our training method selects exemplars that are semantically relevant but lexically distanced from the gold response and weights those exemplars with the relevance score measured by the retriever. through the extensive analysis, including pair-wise human evaluation, we verify that our method improves the performance of existing exemplar-based generative models in terms of appropriateness and informativeness.