Beyond Candidates : Adaptive Dialogue Agent Utilizing Persona and Knowledge (2023)

Abstract: To build ultimate dialogue agents, previous studies suggest models that ground both persona and knowledge. However, applying the dialogue system directly to the usual conversation is still limited because the system requires a complete sentence-formed persona and knowledge candidate sets from the given dataset. In contrast to the dialogue setting in the dataset, humans utilize semantic concepts in their minds rather than a set of pre-defined candidate sentences. Following this manner of human dialogue, we suggest an adaptive dialogue system that is applicable to situations where complete sentence-formed candidates are not given. Our model generates consistent and relevant persona descriptions and identifies relevant knowledge for engaging and knowledgeable responses, even with fragmentary information. We show that our model outperforms previous baselines that utilize persona and knowledge candidate sentences and conduct the human evaluation on the machine-generated responses. In addition, we conduct ablation studies to demonstrate the effectiveness of each component of our model. Furthermore, we apply our model to other dialogue datasets that only ground knowledge or persona to showcase its adaptability. Our code is available at https://github.com/dlawjddn803/BeCand.

## introduction in usual conversations, humans utilize the semantic concept in their minds in terms of the dialogue topic and the preference of the interlocutor. with the semantic-level of concepts, humans communicate each other by aggregating the concepts to convey knowledgeable and empathetic responses @xcite . it implies that people converse by adaptively reorganizing and retrieving additional information with their semantic concepts, encompassing knowledge and persona, not by relying on pre-defined sources @xcite @xcite . it seems that @xcite and @xcite adhere to this human-like approach on the conversation by referring to persona and knowledge. however, it neglects the humans' semantic concept reconstruction and retrieval capability by requiring pre-defined candidate sets to ground as in figure 1 @xcite . as knowledge and persona candidates for the agents are not given in usual conversation, the dependency on the candidates eventually limits their applicability to candidate-free situations as depicted in figure 1 (a). to build the dialogue agents adaptive to the candidate-agnostic situation, two branches of studies are conducted. in knowledge-grounded conversation, the knowledgeable agents employ the non-parametric memory-based retrieval to overcome candidate-agnostic situations @xcite . similarly, personaaware dialogue agents consider the out-of-persona situations by extending persona sentences from a few persona concept @xcite @xcite . even though both streams of research focus on the candidate-agnostic conversational situation, they only leverage a single source for grounding, rather than utilizing both persona and knowledge, simultaneously. in this paper, we propose a dialogue agent utilizing persona and knowledge that is adaptive to the candidate-free situation. to this end, our method consists of 1) a knowledge-retriever 2) a concept-based persona generator, 3) a dialoguepersona aligner, and 4) a response generator. when the knowledge concept is given, a knowledge retriever finds the relevant knowledge from the knowledge base. our concept-based persona generator then produces complete sentences with fragmentary persona concepts. the generated persona descriptions are then validated based on the persona aligner regarding both consistency and relevancy. the validated persona descriptions are used as the input of the response generator. experimental results show that our candidatefree model outperforms other baselines. also, we show that the concept-based persona generator and persona aligner boost the performance of the dialogue agents with the ablation studies. we conduct the human evaluation of our model's responses, and the result implies that our method is effective in building a persona-knowledge dialogue agent without candidate sentences. moreover, we demonstrate that our method is capable of utilizing other dialogue datasets grounding single source, such as personachat @xcite or wizardof-wikipedia (wow) @xcite , and shows the adaptiveness of our proposed model. in qualitative results, it is shown that the generated responses are comparable to the ground truth answers without the given candidates. ## method we propose adaptive dialogue agents that generate the responses without the persona and knowledge candidates. to this end, we assume that the knowledge and persona concepts are only given to the agent for knowledgeable and engaging responses. first, 1) knowledge retriever retrieves the relevant paragraphs with the knowledge concept, and 2) concept-based persona generator produces the persona descriptions with the given short persona concepts. then, 3) persona aligner decides whether the generated persona descriptions are relevant to the dialogue history and whether the sentences are consistent with the previous dialogue history. afterward, 4) response generator provides knowledgeable and engaging responses with the predicted knowledge paragraphs and persona descriptions. ## conclusions in this paper, we introduced an adaptive dialogue agent utilizing persona and knowledge without the given candidates from the dataset. due to the absence of knowledge candidates, the knowledge retriever retrieves the relevant paragraphs with the knowledge concept from the knowledge base. also, the concept-based persona generator outputs the persona descriptions with the fragmentary persona concepts from retrieve-and-generate architecture. the generated persona descriptions are then validated through a persona aligner regarding relevancy and consistency. from experiments, we showed that our method is effective even though the persona concept and knowledge concept are given with the dialogue. we also presented the ablation studies on each component of our model. moreover, we conducted the human evaluation to show the improved quality of the responses of our models and it is also shown in qualitative results. to show its applicability and adaptiveness, we denoted the experimental results of our method on focus, wow, and personachat datasets.