Automatic Evaluation and Moderation of Open-domain Dialogue Systems
Published in
arXiv.org(2021)External Links:
TL
TL;DR
This paper describes the datasets and baselines provided to participants, as well as submission evaluation results for each of the two proposed subtasks, and describes the automatic evaluation mechanisms that show high correlations with human judgements across multiple dialogue evaluation aspects.
Abstract
The development of Open-Domain Dialogue Systems (ODS)is a trending topic due to the large number of research challenges, large societal and business impact, and advances in the underlying technology. However, the development of these kinds of systems requires two important characteristics:1) automatic evaluation mechanisms that show high correlations with human judgements across multiple dialogue evaluation aspects (with explainable features for providing constructive and explicit feedback on the quality of generative models' responses for quick development and deployment)and 2) mechanisms that can help to control chatbot responses,while avoiding toxicity and employing intelligent ways to handle toxic user comments and keeping interaction flow and engagement. This track at the 10th Dialogue System Technology Challenge (DSTC10) is part of the ongoing effort to promote scalable and toxic-free ODS. This paper describes the datasets and baselines provided to participants, as well as submission evaluation results for each of the two proposed subtasks.
References58 items
1
Investigating the Impact of Pre-trained Language Models on Dialog Evaluation
2
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators’ Disagreement
3
Internet-Augmented Dialogue Generation
4
Beyond Goldfish Memory: Long-Term Open-Domain Conversation
5