Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation (2022)

Abstract: In this paper we detail the implementation of Proto-Gen, an end-to-end neural response generator capable of selecting appropriate persona and fact sentences from available options, and generating persona and fact grounded responses. Incorporating a novel interaction layer in an encoder-decoder architecture, Proto-Gen facilitates learning dependencies between facts, persona and the context, and outperforms existing baselines on the FoCus dataset for both the sub-tasks of persona and fact selection, and response generation. We further fine tune Proto-Gen’s hyperparameters, and share our results and findings.

## introduction with the growth of neural methods for language modelling, the task of response generation in the field of open domain dialogue and interactive systems have witnessed significant improvements. incorporating transformer @xcite based architectures with billions of parameters, and trained on large training corpora, such models @xcite @xcite have advanced the state-ofthe-art in response generation. however, trained with the objective of generating the next response by conditioning only on the context, such models often result in unnatural and hallucinated responses @xcite , which if not addressed appropriately, hampers it's usefulness in practical settings @xcite . although recent years have witnessed advancements in response generators which can factor in external knowledge @xcite and exhibit certain humanlike features like personality traits, emotions, .etc @xcite @xcite , research in response generators that can generate user-centric responses by factoring both user persona and ex-ternal knowledge is still an unsolved problem. in this paper we propose proto-gen, an end-to-end response generator that can select the most appropriate fact and user persona sentences based on the conversation context, and generate a response customized for the user. ## task an data description the task aims at engendering intelligent response generators that can generate appropriate response to user queries by factoring in the user's persona along with available external facts. it is further divided into two sub-tasks: with the inputs being 5 persona candidates of the user, 10 knowledge candidates pertaining to the topic of discussion, and the conversation context, this sub-task requires predicting the correct persona and knowledge sentence which can be used for generating the response. the dataset @xcite comprises 14,452 persona-knowledge dialogues (11,562 training, 1,445 validation, and 1,445 testing) pertaining to discussions about landmarks such as statue of liberty, eiffel tower, the great wall, etc. ## methods as illustrated in figure 1 , we implement an end-toend encoder-decoder based architecture for jointly performing all sub-tasks. below we discuss each component in detail. ## conclusion here we detail proto-gen, an end-to-end neural response generator, that can not only select appropriate persona and fact sentences from available input options, but also generate persona and knowledge grounded responses. incorporating a novel interaction layer which includes biaffine classifiers and trained on the focus dataset, proto-gen outperforms existing external baselines for all sub-tasks. we further perform experiments to fine tune proto-gen's hyperparameters, and report our results. generation. in proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations, pages 270-278, online. association for computational linguistics.