PK - ICR : Persona-Knowledge Interactive Multi-Context Retrieval for Grounded Dialogue (2023)

Abstract: Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation. However, each grounding has been mostly researched in isolation with more practical multi-context dialogue tasks introduced in recent works. We define Persona and Knowledge Dual Context Identification as the task to identify persona and knowledge jointly for a given dialogue, which could be of elevated importance in complex multi-context dialogue settings. We develop a novel grounding retrieval method that utilizes all contexts of dialogue simultaneously. Our method requires less computational power via utilizing neural QA retrieval models. We further introduce our novel null-positive rank test which measures ranking performance on semantically dissimilar samples (i.e. hard negatives) in relation to data augmentation.

## introduction effective conversation agents require external context as grounding information to enhance response generation. there has been much progress on each persona @xcite @xcite and knowledge @xcite @xcite grounded dialogue systems respectably. however, the combination of both and more unique contexts has not been studied, with limited interest in industry persona-based qa systems @xcite . @xcite ; @xcite ; moghe et al. @xcite have shown the importance of directly optimizing knowledge extraction in dialogue, while @xcite ; @xcite ; @xcite have shown the importance of directly optimizing for concrete persona. we further argue that in practical settings, it is more realistic to * please correspond to minsik@mskresearch.org. ## related works to develop dialogue systems that rely on external knowledge information, open-domain dialogue datasets of wizard of wikipedia @xcite and personachat @xcite are most commonly studied. they consist of conversations that are grounded by wikipedia knowledge and persona profile information, respectively. more recent datasets consist of conversations grounded by both persona & knowledge @xcite or blended scenarios @xcite . in line with the prior works, we treat persona and knowledge as different groundings with distinct characteristics and investigate semantic relations. integrating either persona or knowledge bases with dialogue agents in isolation has been actively studied. zhang et al. (2018a); majumder et al. (2020); xu et al. (2020); rashkin et al. (2019) for persona, and dinan et al. (2018); zhao et al. (2020); liu et al. (2021); li et al. (2020); ghazvininejad et al. (2017) for knowledge. persona-only method prohibits elaboration with detailed knowledge. in contrast, relevant knowledge might depend on the persona of the user. we address the limitations by studying all dialogue component interactions. knowledge identification @xcite task has been defined in recent papers stemming from knowledge-grounded dialogue. our work aligns with the view in @xcite that context identification is a separately important task in an interactive dialogue setting, with similarities to open question answering @xcite and industry persona-based qa systems @xcite . our research expands upon the knowledge identification task to specify persona & knowledge as dual contexts to be jointly retrieved from the dialogue. ## methodology we maximize interactions between all components of a conversation turn for effective retrieval of dialogue groundings. knowledge retrieval is a top-1 ranking task (section 3.1), and persona retrieval is a point-wise scoring task with 1 or 0 true persona label (section 3.2). we solve knowledge retrieval in a zero-shot manner, while we introduce null-positive rank test to investigate the hard-negative traits of persona-augmented dialogue (section 3.3). foot_2 ## experiment setup we utilize the call for customized conversation @xcite dataset for which each conversation is built with both the user's persona and wikipedia knowledge grounding information. we utilize multiple neural qa models trained on ms marco dataset @xcite . more details in appendix d. ## discussions and conclusion we introduce persona-knowledge dual context retrieval method pk-icr in this paper. we perform qa-informed prompt-augmentations of data that we report movements of delta in correct directions for rank -1, 0 and ranks with long distance 3, 4. similar results for bi-encoder (fig. f1 ). as the nlp community aims to tackle more complex dialogue systems, our methods may be further enhanced by sophisticated grounding contexts and interactions present in dialogue. considering all components of dialogue, being persona, knowledge, and dialogue in a travel agent scenario, is crucial to obtain each grounding context required for accurate responses. we suggest two future directions for dialogue systems. our persona-aware dialogue augmentation ( 1 ⃝ in figure 1 ) is a form of modeling the human behavior in regards to turing test @xcite . we isolate effectiveness of our augmentation with successful null-positive rank test (section 3.3). we suggest two future directions regarding persona-dialogue augmentation. finally, our novel null-positive rank test (nrt, section 3.3) is widely applicable to information retrieval and ranking models. we only trained the models with positive and negative dialogue augmentations, but interestingly, we report that the model improves ranking correctness for "neutral" (non-augmented) dialogue. this evaluation method could be directly used to compute whether the model obtains sophisticated ranking capabilities. this would be especially relevant in the context of data augmentation or other ranking tasks, including personalized search / qa. we recommend performing our test for future works.