Adding Chit-Chat to Enhance Task-Oriented Dialogues (2021)

Abstract: Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations. In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and interactive. Specifically, we propose a Human <-> AI collaborative data collection approach for generating diverse chit-chat responses to augment task-oriented dialogues with minimal annotation effort. We then present our new chit-chat-based annotations to 23.8K dialogues from two popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation. Lastly, we propose three new models for adding chit-chat to task-oriented dialogues, explicitly trained to predict user goals and to generate contextually relevant chit-chat responses. Automatic and human evaluations show that, compared with the state-of-the-art task-oriented baseline, our models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike, while maintaining competitive task performance.

## introduction with modeling innovations, increasing computing power, and a growing number of datasets, recent years have witnessed significant improvements in the performance of both task-oriented dialogue systems and chit-chat systems @xcite @xcite . most research on dialogue * work done as a research intern at facebook. the code and data are available at https://github.com/ facebookresearch/accentor . i'm looking for a concert in vancouver. i found an event for the beach boys at pne amphitheatre. ## data construction in this section, we describe an approach to gather supervisory data for adding contextually relevant chit-chat to task-oriented dialogues. our approach needs minimal annotation effort to augment suitable and diverse chit-chat add-ons that are not available in existing task-oriented datasets (section 5.1). we primarily report results based on dialogues from the sgd dataset in this study, because it is the largest task-oriented dialogue dataset and is generally cleaner compared with most other task-oriented dialogue datasets. however, our approach is flexible and thus not limited to dialogues from a particular task-oriented dataset (section 4.1). figure 2 shows the overview of our approach. ## conclusion we propose adding chit-chat to enhance taskoriented dialogues (accentor) in this study. we present a general human↔ai collaborative data construction approach for accentor, with which we create a dataset consisting of 23.8k chit-chat augmented task-oriented dialogues. we show via human evaluation that chit-chat augmented dialogues are preferred than the unaugmented. in addition, we propose three models for accentor. evaluation results show that compared with the baseline trained on the original unaugmented data, our proposed models trained on the chit-chat augmented counterpart achieve a similar task performance level and higher human evaluation scores.