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While dialogue remains an important end-goal of natural language research,the difficulty of evaluation is an oft-quoted reason why it remains troublesometo make real progress towards its solution. Evaluation difficulties areactually two-fold: not only do automatic metrics not correlate well with humanjudgments, but also human judgments themselves are in fact difficult tomeasure. The two most used human judgment tests, single-turn pairwiseevaluation and multi-turn Likert scores, both have serious flaws as we discussin this work.  We instead provide a novel procedure involving comparing two full dialogues,where a human judge is asked to pay attention to only one speaker within each,and make a pairwise judgment. The questions themselves are optimized tomaximize the robustness of judgments across different annotators, resulting inbetter tests. We also show how these tests work in self-play model chat setups,resulting in faster, cheaper tests. We hope these tests become the de factostandard, and will release open-source code to that end.

ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons