Open-Domain Dialogue Quality Evaluation: Deriving Nugget-level Scores from Turn-level Scores
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Abstract (arXiv)
Existing dialogue quality evaluation systems can return a score for a given system turn from a particular viewpoint, e.g., engagingness. However, to improve dialogue systems by locating exactly where in a system turn potential problems lie, a more fine-grained evaluation may be necessary. We therefore propose an evaluation approach where a turn is decomposed into nuggets (i.e., expressions associated with a dialogue act), and nugget-level evaluation is enabled by leveraging an existing turn-level evaluation system. We demonstrate the potential effectiveness of our evaluation method through a case study.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 30 Sep 2023.
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14 of 24 references.
References · 14
- 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
- 2019What makes a good conversation? How controllable attributes affect human judgmentscited by 140
- 2020Unsupervised Evaluation of Interactive Dialog with DialoGPTcited by 139
- 2019Survey on Evaluation Methods for Dialogue Systemscited by 126
- 2018Conversational Recommender Systemcited by 115
- 2021A Comprehensive Assessment of Dialog Evaluation Metricscited by 90
- 2021DynaEval: Unifying Turn and Dialogue Level Evaluationcited by 59
- 2020Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systemscited by 47
- 2019Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systemscited by 36
- 2019Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluatorscited by 34
- 2019Generating Empathetic Responses by Looking Ahead the User's Sentimentcited by 27
- 2022EnDex: Evaluation of Dialogue Engagingness at Scalecited by 6
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