On the Limits of Minimal Pairs in Contrastive Evaluation (2021)

Abstract: Minimal sentence pairs are frequently used to analyze the behavior of language models. It is often assumed that model behavior on contrastive pairs is predictive of model behavior at large. We argue that two conditions are necessary for this assumption to hold: First, a tested hypothesis should be well-motivated, since experiments show that contrastive evaluation can lead to false positives. Secondly, test data should be chosen such as to minimize distributional discrepancy between evaluation time and deployment time. For a good approximation of deployment-time decoding, we recommend that minimal pairs are created based on machine-generated text, as opposed to human-written references. We present a contrastive evaluation suite for English–German MT that implements this recommendation.

## introduction contrastive evaluation is one of the most widely used evaluation techniques for generative language models, both for causal models @xcite and sequence-to-sequence models @xcite . various phenomena have been analyzed using this technique, including syntax @xcite ; among others), word sense disambiguation @xcite ; among others), document coherence @xcite ; among others), and grammatical acceptability in general @xcite . contrastive evaluation allows for a targeted, automated evaluation of generative models but is restricted to a specific behavioral interface, namely the ranking of pre-defined minimal pairs. however, most models in application areas such as translation or conversation are deployed to produce 1-best sequences, exposing a different behavioral interface to users. while this limitation of contrastive evaluation is well known, its practical relevance has been unclear. we show that under certain conditions, the gap between evaluation and deployment can indeed cause misleading results. as a main factor we identify the distributional discrepancy of contrastive evaluation datasets: minimal pairs are usually derived from human-written references, but when deployed, a model is conditioned on its own output. to measure the effect of this factor on evaluation, we focus on neural machine translation (nmt) systems. our approach is to test implausible research hypotheses in addition to plausible ones. we find that distributional discrepancy increases the number of false positives regarding implausible hypotheses. they particularly occur when evaluating distilled nmt models @xcite , indicating that in such models, ranking behavior on noisy sequences diverges from generative behavior. we also propose a way to reduce the distributional discrepancy of minimal pairs. our experiments show that false positives can be largely avoided by using machine-generated text instead of human-written text. this inspires us to release distillingeval, a variant of the lingeval97 english-german mt evaluation suite @xcite that uses mt-generated references. we recommend that future efforts to create contrastive datasets for the evaluation of language generation models minimize distributional discrepancy between evaluation and deployment. due to the possibility of false positives, linguistic conclusions about knowledge or abilities of models should be corroborated by additional evidence from a more natural setting. ## experiments in previous work, contrastive evaluation has commonly been used to test plausible research hypotheses, for example the hypothesis that rnns can predict long-distance number agreement @xcite , or the hypothesis that word dropout improves pronoun resolution in translation @xcite . in this paper, we are interested in implausible hypotheses and in how the testing of such hypotheses is affected by the limitations described in the previous section. we formulate two implausible hypotheses about nmt systems, which we mark with an asterisk (*): nmt systems make liberal use of vague placeholder words. specifically, english-german models use the german placeholder noun ding ('thing') ubiquitously. examples are given in the next section. the two hypotheses are chosen because they seem implausible both theoretically and empirically. from a theoretical standpoint, both linguistic phenomena rarely occur in the training data and the model is unlikely to adopt them broadly. furthermore, the cognitive and social factors that cause the phenomena in human speech do not apply to neural language models. empirically, we find that both phenomena are indeed very rare in neural machine translations, independent of model quality. for comparison, we also test two plausible hypotheses about nmt systems: ## dataset release given the improved specificity of test sets with machine-generated references, we release corresponding test sets for other phenomena in the lingeval97 test suite @xcite , terming our dataset variant distillingeval. lingeval97, the original test suite, is a collection of 97k contrastive translation pairs for 13 different error types in english-german translation. building on lingeval97, we create test sets with ## discussion by testing implausible hypotheses, we demonstrate the risk of drawing wrong inferences about generative behavior of (conditional) language models, especially if there is a large distributional discrepancy between minimal pairs and generated sequences. this problem is especially apparent for distilled nmt models, which perform poorly on humanwritten minimal pairs because they were never exposed to such a distribution during training. while this indicates that distilled nmt models are less robust against improbable contexts, human-crafted minimal pairs also become less useful to predict their unconstrained generative behavior. the danger of false positives from minimal pairs highlights the fact that behaviorist approaches to measuring knowledge are limited to the behavioral interface that is observed. systematic assessments of linguistic knowledge or syntactic abilities of neural models should be qualified accordingly, in case minimal pairs are the primary analysis method. we suspect that whenever a broad range of hypotheses is tested, including phenomena that are rarely observed in actual machine-generated text, the risk of false positives is increased. ## conclusion we show that there are conditions where contrastive evaluation leads to false positives if generative behavior is inferred from behavior under forced choice. experiments with english-german nmt indicate that the gap between the two behavioral interfaces is especially high when human-written text is used to create minimal pairs. using machinegenerated text largely reduces the gap. we recommend that human-written minimal pairs are mainly used for assessing the robustness of models, but that for predicting the generative behavior of language models, machine-generated minimal pairs are used.