Evaluating Coherence in Dialogue Systems using Entailment
This addresses the challenge of expensive and biased evaluation in dialogue systems, offering a more efficient and consistent method for researchers and developers.
The paper tackles the problem of evaluating open-domain dialogue systems by introducing interpretable metrics for topic coherence using distributed sentence representations and approximating human judgment with state-of-the-art entailment techniques, resulting in metrics that serve as a scalable and unbiased surrogate for human evaluation on large-scale datasets.
Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time consuming, and not scalable. Moreover, judges tend to evaluate a small number of dialogues, meaning that minor differences in evaluation configuration may lead to dissimilar results. In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations. Furthermore, we introduce calculable approximations of human judgment based on conversational coherence by adopting state-of-the-art entailment techniques. Results show that our metrics can be used as a surrogate for human judgment, making it easy to evaluate dialogue systems on large-scale datasets and allowing an unbiased estimate for the quality of the responses.