KETOD : Knowledge-Enriched Task-Oriented Dialogue (2022)

Abstract: Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse naturally and seamlessly with users, it is important to build a dialogue system that conducts both types of conversations effectively. In this work, we investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model. To this end, we create a new dataset, KETOD (Knowledge-Enriched Task-Oriented Dialogue), where we naturally enrich task-oriented dialogues with chit-chat based on relevant entity knowledge. We also propose two new models, SimpleToDPlus and Combiner, for the proposed task. Experimental results on both automatic and human evaluations show that the proposed methods can significantly improve the performance in knowledge-enriched response generation while maintaining a competitive task-oriented dialog performance. We believe our new dataset will be a valuable resource for future studies. Our dataset and code are publicly available at https://github.com/facebookresearch/ketod .

## introduction dialogue systems have achieved substantial progress @xcite @xcite due to recent success in language model pre-training @xcite @xcite . one major type of dialogue being studied is task-oriented dialogue (tod) @xcite @xcite , where the system aims to collect user intents/goals to complete certain tasks (e.g. restaurant-booking). in most of tod systems, the system responses are concise and templated, as what type of event do you prefer? ## approaches in this section, we will describe the proposed two models for the ketod dataset. ## experimental results baseline model. we use simpletod @xcite ) as our baseline model, i.e., with the training sequence as [ c, b, d, a, t ], without the injection of knowledge snippets. therefore the knowledge-grounded chit-chat in the responses t do not have any knowledge groundings -we aim to show the necessity of knowledge grounding for our task, as well as the effectiveness of our proposed models to incorporate knowledge. experimental setups and evaluations. check appendix b for details of model training and parameter settings. for the tod performances, we evaluate the belief states with joint goal accuracy (joint ga) and average goal accuracy (avg ga), and the actions with act-slot f1, same as @xcite . for the automatic evaluations of response generation, we use three bleu-4 scores: bleu-4 aug for evaluating the responses enriched with knowledge; bleu-4 orig for evaluating the responses not enriched with knowledge; bleu-4 all for evaluating all responses; ## ethical considerations data access and licensing. we develop the ke-tod dataset based on the publicly available sgd dataset 2 @xcite . the sgd dataset is publicly available under the cc-by-sa-4.0 license.