Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the W inograd Schema (2021)

Abstract: The Winograd Schema (WS) has been proposed as a test for measuring commonsense capabilities of models. Recently, pre-trained language model-based approaches have boosted performance on some WS benchmarks but the source of improvement is still not clear. This paper suggests that the apparent progress on WS may not necessarily reflect progress in commonsense reasoning. To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation. We also propose two new baselines that indicate the existence of artifacts in WS benchmarks. We then develop a method for evaluating WS-like sentences in a zero-shot setting to account for the commonsense reasoning abilities acquired during the pretraining and observe that popular language models perform randomly in this setting when using our more strict evaluation. We conclude that the observed progress is mostly due to the use of supervision in training WS models, which is not likely to successfully support all the required commonsense reasoning skills and knowledge.

## introduction the winograd schema (ws) @xcite was proposed as an alternative to the turing test, by virtue of evaluating progress on commonsense reasoning. the task is a multi-choice question akin to coreference resolution. given a text snippet with two entities and a pronoun that refers to one of the entities, select the entity referred to by the pronoun. foot_1 consider the following example: ## a robust group score evaluation many works in recent years have shown that large neural networks can achieve high performance on different benchmarks while "being right for the wrong reasons" @xcite . these successes arise from a variety of reasons such as artifacts in datasets @xcite @xcite , annotators biases @xcite , etc. @xcite proposed to alleviate some of these issues by using the twin sentences along with the special word. however, the proposed evaluation of wsc scores each twin separately. as @xcite showed that some wsc instances can be solved using simple correlations, we argue that the independent scoring may result in unjustifiably inflated scores. here, we inspect a new evaluation that accounts for some of these artifacts and provide a more robust evaluation for cases where we have grouped instances (e.g. minimal pairs). ## setup datasets we experiment with two english ws datasets: winograd schema challenge (wsc) @xcite contains 273 manually curated examples. we also report results on the non-associative examples that were filtered by @xcite , named wsc-na. winogrande @xcite is a recent crowdsourced dataset that contains ws questions. winogrande contains 40,938, 1,267, 1,767 examples for train, development, and test respectively. since the test labels were not published, we report our results on the development set. we provide a more detailed description of these datasets and splits in appendix a. ## artifacts-detecting baselines for ws wsc was carefully designed by human experts to minimize the presence of artifacts. for instance, example 3 is not considered as a good ws example since a racecar is more likely to go fast rather than a school bus. however, such correlations are often easy to miss. as evidence, @xcite found 37 sentences to be associative, or non googleproof. 8 these examples were labeled manually using crowdsourcing, therefore these are still bound to what non-experts can catch, and subtler cues may be hard to spot. other correlations may be harder or impossible to detect by humans since they are the result of spurious correlations @xcite . these features, which can be learned during pretraining or fine-tuning, may result in successful predictions that do not reflect commonsense reasoning skills. to account for these artifacts, we propose two control baselines, which are likely to achieve random performance with an artifacts-free model. a score above random indicates the presence of artifacts. no-candidate baseline this baseline (nocands) removes the two candidates (entities) from the text. for instance, example 1 will turn into: "would not fit into because it is too large." partial-sentence baseline in this baseline (partsent) we split the sentence into two parts, based on punctuation and discourse markers foot_8 and take only the part containing the pronoun. for instance, example 1 will be transformed into the following: "because it is too large." a similar approach was used by @xcite , however, they employed annotators to manually indicate whether the partial sentence containing the pronoun is associative to one of the candidates. alternatively, we use a trained model and inspect the overall score on a dataset. we note that these two baselines create nonsensical sentences. therefore, we expect humans to not be able to properly solve them. thus, a model that achieves higher than random performance on these baselines over a large enough dataset is suspected to rely on spurious correlations. these baselines are reminiscent of previous works that used part of the input (e.g. the hypothesis only baseline in nli), to reveal artifacts in multiple datasets for nli @xcite and reading comprehension @xcite . the trophy doesn't fit into the brown suitcase because it is too large. ## disentanglement of commonsense reasoning and learned commonsense in this section, we wish to disentangle the commonsense reasoning skills acquired by plms during pretraining, and what they learn during fine-tuning on a ws dataset. we propose a method that allows evaluating pretrained masked language models (mlm) in a zero-shot setting on ws-like questions. ## conclusions in this work, we begin by discussing the current evaluation of ws and propose an additional evaluation metric, group-scoring, that credits a model with the worse performing instance of a group. while we focus here on ws, we propose to use this evaluation in other tasks, where minimal pairs are available @xcite @xcite , as a more reliable evaluation metric. we then propose two new control baselines that account for artifacts in ws data and show that wsc contains many artifacts, while winogrande consists much less of them. finally, we propose a method to evaluate mlms on ws sentences in a zero-shot setting. we show that the performance of popular mlms is random and that models improve gradually the more training data they see. we conclude that the use of large training sets is not always desirable, especially in commonsense reasoning settings, and call future work to find other methods to improve our models' commonsense abilities.