| Challenge: | Existing automated dialogue response evaluators have only moderate correlation with human judgement and are not robust. |
| Approach: | They propose to build a reference-free dialogue response evaluator that exploits the power of semi-supervised training and pretrained (masked) language models. |
| Outcome: | The proposed model achieves strong correlation with human judgement and generalizes robustly to diverse responses and corpora. |
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| Challenge: | Existing tools for dialogue evaluation do not generalize to unseen datasets and/or need a human-generated reference response during inference. |
| Approach: | They propose an unreferenced automated dialogue evaluation metric that uses large pre-trained language models to extract latent representations of utterances and leverages the temporal transitions that exist between them. |
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SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (2022.coling-1)
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| Challenge: | Existing evaluation metrics are expensive and easy to conduct but ineffective to reflect dialogue quality. |
| Approach: | They propose a self-supervised fine-grained dialogue evaluation framework which can automatically assign fine-granular scores for arbitrarily dialogue data. |
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uBLEU: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems (2020.acl-srw)
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| Challenge: | Existing evaluation metrics for text generation tasks do not consider uncertain responses without writing additional reference responses by hand. |
| Approach: | They propose a human-aided, uncertainty-aware evaluation method for open-domain dialogue systems, BLEU. |
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Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020.acl-main)
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| Challenge: | Existing methods of open-domain dialogue evaluation are labor-intensive and inefficient. |
| Approach: | They propose to use open-domain dialogues to evaluate different aspects of dialogues using holistic evaluation metrics. |
| Outcome: | The proposed metrics show strong correlations with human judgments. |
xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark (2023.findings-emnlp)
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| Challenge: | Currently, human evaluation is the most reliable way to holistically judge the quality of the dialogue. |
| Approach: | They propose to use English dialogue evaluation metrics to generalize them to other languages. |
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Enhancing the Open-Domain Dialogue Evaluation in Latent Space (2021.findings-acl)
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| Challenge: | Existing methods to evaluate opendomain dialogues are limited due to the one-to-many nature of dialogues. |
| Approach: | They propose a self-supervised setting to obtain a smooth latent space that captures discourse-level context information and implicitly models more references in latent spaces. |
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RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue (2023.acl-long)
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| Challenge: | Evaluating open-domain dialogue systems is challenging because of the one-to-many problem. |
| Approach: | They propose a reference-based dialogue evaluation approach that leverages the pre-created utterance as reference other than the gold response to relieve the one-to-many problem. |
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Measuring the Robustness of Reference-Free Dialogue Evaluation Systems (2025.coling-main)
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| Challenge: | Advancements in dialogue systems powered by large language models have outpaced the development of reliable evaluation metrics. |
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Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach (2021.emnlp-main)
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| Challenge: | Existing methods for evaluation of dialog systems are expensive and not scalable . a framework for estimating human evaluation scores is proposed to bridge this gap . |
| Approach: | They propose a framework for estimating human evaluation scores based on off-policy evaluation . they use language quality metrics for single-turn response generation given a fixed context . |
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Achieving Reliable Human Assessment of Open-Domain Dialogue Systems (2022.acl-long)
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| Challenge: | Evaluation of open-domain dialogue systems is challenging and unreliable . human evaluation of live conversations is highly reliable, but reliability cannot be assumed . |
| Approach: | They propose a method of open-domain dialogue evaluation that is highly reliable . they compare live conversations with models that avoid pre-created reference dialogues . |
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