On the Limitations of Reference-Free Evaluations of Generated Text

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10960 - 10977 December 7-11, 2022 ©2022 Association for Computational Linguistics On the Limitations of Reference-Free Evaluations of Generated Text Daniel Deutsch,∗† Rotem Dror,‡ and Dan Roth‡ †Google Research ‡University of Pennsylvania dandeutsch@google.com {rtmdrr,danroth}@seas.upenn.edu Abstract There is significant interest in developing eval- uation metrics which accurately estimate the quality of generated text without the aid of a human-written reference text, which can be time consuming and expensive to collect or en- tirely unavailable in online applications. How- ever, in this work, we demonstrate that these reference-free metrics are inherently biased and limited in their ability to evaluate gener- ated text, and we argue that they should not be used to measure progress on tasks like ma- chine translation or summarization. We show how reference-free metrics are equivalent to us- ing one generation model to evaluate another, which has several limitations: (1) the metrics can be optimized at test time to find the ap- proximate best-possible output, (2) they are in- herently biased toward models which are more similar to their own, and (3) they can be biased against higher-quality outputs, including those written by humans. Therefore, we recommend that reference-free metrics should be used as diagnostic tools for analyzing and understand- ing model behavior instead of measures of how well models perform a task, in which the goal is to achieve as high of a score as possible.1 1 Introduction Automatically evaluating the quality of generated texts is essential for the development of natural lan- guage generation systems. The most common type of evaluation for generation tasks such as machine translation (MT) and summarization is done with reference-based automatic metrics, which evaluate a text by comparing it to a gold-standard reference text, usually written by humans (Papineni et al., 2002; Lin, 2004; Zhang et al., 2020; Sellam et al., 1https://cogcomp.seas.upenn.edu/page/ publication_view/991 ∗Work done while at the University of Pennsylvania Prism-src (↑) Source Doch er ist nicht krank, er hat nur einen mächtigen Kater. Reference But he is not ill, he only has quite a hangover. -1.6 Candidate But he is not sick, he has only one powerful cat. -0.4 Source Und mit Mann und Maus gegen Mainz verteidigt. Reference And threw everything they had into our defense. -4.8 Candidate And defended with man and mouse against Mainz. -0.4 Figure 1: Here, Prism-src was optimized to generate the candidate translations. They are clearly wrong (Kater means both “cat” and “hangover”; mit Mann und Maus is an expression that means “with all means available”), but have better Prism-src scores than the references. Comparing systems with reference-free metrics will favor systems that are more similar to the metrics’ un- derlying models rather than higher quality output. 2020; Deutsch et al., 2021a; Zhang and Bansal, 2021, inter alia). Reference texts can be expensive to collect or are entirely unavailable when there is a need to estimate the quality of text in real time, so there is an increased interest in developing …

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