You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge (2022)

Abstract: To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. We conduct experiments on the persona-knowledge chat and achieve state-of-the-art performance in grounding and generation tasks on the automatic metrics. Moreover, we validate the answers from the models regarding hallucination and engagingness through human evaluation and qualitative results. We show our retriever’s effectiveness in extracting relevant documents compared to the other previous retrievers, along with the comparison of multiple candidate scoring methods. Code is available at https://github.com/dlawjddn803/INFO

## introduction to build an ultimate conversational agent that interacts with humans fluently, previous studies provide generative neural network-based models @xcite . although the answers generated from those models are plausible, they lack informativeness and engagingness resulting in bland responses compared to humans @xcite ., * equal contributors § corresponding author dialogue human: is it in england? machine: no, it is actually in scotland where you are going. human: where in scotland? human's persona i will travel through north ayrshire. i am going to scotland. i like history. i am interested in architecture. i love to garden. ## method to generate more knowledgeable and engaging dialogue, we introduce our conversational model that grounds external knowledge and persona information as in figure 1 . we first encode the input with the pre-trained language model, and then choose the proper knowledge and persona from the given candidates for each selector. we employ poly-encoder @xcite as knowledge selector and persona selector to exploit its enhanced capability of capturing relevance between candidate set and context (i.e., dialogue history). then, the predicted persona and knowledge are aligned into one sequence to the dialogue history for consistency between grounding and generation. the sequence is defined as a knowledge-persona enhanced query (kpeq), then it feeds into the retriever-augmented generator (rag). the generator then extracts the relevant paragraphs to refer from the knowledge index to reduce hallucination. ## conclusions in this paper, we presented a conversational agent that generates responses grounding the user's persona and external knowledge. we utilized poly-encoder-based candidate scoring for each grounding task. we additionally implement persona level indicator to consider multiple persona selections for delicate persona grounding. with predicted sources, we construct a knowledge-persona enhanced query to retrieve latent paragraphs, and they are used to generate informative and engaging responses by marginalizing loss for each token. we show that our method achieves the state-of-the-art (sota) score in both grounding and generation tasks in the persona-knowledge conversation dataset. we also demonstrate that the responses from info show less hallucination and more engagingness through human evaluation and qualitative analysis. we also compare the grounding modules and retrievers to show info's effectiveness.