Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality (2022)

Abstract: Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning. Yet, they fail miserably on the recently proposed Winoground dataset, which challenges models to match paired images and English captions, with items constructed to overlap lexically but differ in meaning (e.g., “there is a mug in some grass” vs. “there is some grass in a mug”). By annotating the dataset using new fine-grained tags, we show that solving the Winoground task requires not just compositional language understanding, but a host of other abilities like commonsense reasoning or locating small, out-of-focus objects in low-resolution images. In this paper, we identify the dataset’s main challenges through a suite of experiments on related tasks (probing task, image retrieval task), data augmentation, and manual inspection of the dataset. Our analysis suggests that a main challenge in visuolinguistic models may lie in fusing visual and textual representations, rather than in compositional language understanding. We release our annotation and code at https://github.com/ajd12342/why-winoground-hard.

## introduction despite the success of large pretrained transformer models on a wide variety of tasks, the extent to which they are compositional (e.g., @xcite @xcite @xcite and grounded @xcite is debated. taking compositionality and groundedness as key desiderata, the recent winoground dataset @xcite provides a clever way to test multimodal vision and language models. given two images and two captions, the goal is to pair them correctly. the key insight is *co-first authors contributed equally. order determined by coin flip. fine-grained tags, identify their main challenges, and evaluate performance separately on each subcategory (section 4); further, we (c) also create textual variants of the original captions where they are no longer minimal semantic pairs. models are still unable to succeed on the winoground task (section 5) when given such linearly separable pairs. citing evidence from sinha et al. (2021a) that large language models don't need word order information to do well on tasks (see also @xcite @xcite @xcite , the winoground authors suggest that models track word co-occurrences, thus giving "the illu-sion of an understanding of word order" without actually achieving that understanding @xcite . indeed, given that information about semantic meaning can be uncovered without word order information (e.g., @xcite and that seemingly syntactic and semantic tasks can be solved with lexical heuristics (e.g., @xcite , winoground failures may offer another evidence that language models solve complex tasks in a relatively superficial way. to assess this possibility, we examine the winoground task and conduct a series of novel experiments on the dataset, testing three models (clip, @xcite ; uniter, @xcite ; lxmert, tan and bansal 2019) that reflect three broad categories of transformer-based vision-and-language architectures. first, we test these models on the more general (and standard) text-to-image and image-to-text recall@k task using the winoground images and captions. some models fail at this simpler task, suggesting that failure on winoground may not be just because of a failure in semantic composition but due to broader difficulty with atypical images in the dataset. we show that even fine-tuning probes specifically on winoground does not help, implying a potential absence of information necessary to succeed at the task. second, to understand what the source of failure might be, we develop a new taxonomy of winoground examples consisting of six classes (section 4). our taxonomy reflects various abilities required to solve the task, and model's performances vary significantly among our classes. given an explosion of interest in testing image generation models (e.g., dall-e 2 and imagen) on their compositional ability (e.g., @xcite , winoground can be a crucial benchmark, which motivates the need for a deeper analysis of its properties; prior work has performed such deep analyses for other benchmarks @xcite @xcite . we tag every example in winoground with our scheme (see appendix e for full reporting) and provide a performance breakdown of models. figure 1(b ) shows these tags. we show high variability in performances based on our proposed tags and observe low performance on tags that are challenging for reasons beyond compositional language understanding (e.g., a low-res version of the image simply lacks the visual detail necessary for answer-ing the question). thus, we conclude that not all winoground items test what they aim to, and identify a subset of 171 items which directly measure compositionality. third, we run a series of probing experiments to better understand whether the failure arise because of failures in visual discrimination, in linguistic compositionality, or in the fusion of vision and language. specifically, we augment the original captions with a set of textual variants @xcite . while these textual variants are indeed highly separable in embedding space, using them fails to improve the task performance. figure 1(c) shows these textual variants. taken together, our results suggest that failures found on winoground reflect meaningful model failures. while some winoground items may be illsuited to evaluate compositionality, even the most straightforward items pose a challenge. our evidence suggests that the source of these robust failures lies in fusing visual and linguistic information, not strictly in complex language understanding. we hope our analysis will help future endeavors in interpreting emerging models' winoground performance. ## background the winoground dataset @xcite contains 400 items (each consisting of two im-age+text pairs with overlapping lexical content). the items were categorized linguistically based on whether the text swaps an object, a relation, or both. the items were further categorized based on if they involved: a pragmatics tag indicating nonliteral/pragmatic reasoning required, a symbolic tag indicating reasoning about something in symbolic space (e.g., children's drawing), and a series tag (indicating whether the items come from the same, as opposed to from unrelated, photos). evaluated models see one image/caption pair at a time for a given item, where an item consists of two pairs: i 0 and its paired caption c 0 , and i 1 and its paired caption c 1 . they then compute an image score, text score, and group score (by scoring each item as either 1 or 0 and then aggregating). for a given pair, the image score is 1 if and only if for image i 0 a higher score is assigned to caption c 0 than c 1 and for i 1 a higher score is assigned to c 1 than c 0 . similarly, the text score is 1 if and only if for text c 0 a higher score is assigned to i 0 than i 1 (and vice versa for c 1 ). thus, for both the image and text score, random chance is 1/4. an item's group score is 1 if and only if both its text and image scores are 1. the random chance for group score is 1/6. ## relaxing winoground constraints these metrics are relatively harsh in two respects. first, they require perfect matching between the images and captions, implicitly evaluating an unusual variant of recall: recall @ 1 over 2 candidates (i.e., the best image must be ranked first between the two candidates). further, they do not allow any adaptation to the task (i.e., zero-shot transfer is required). we therefore relax each of these two constraints in turn. ## characterizing the challenges presented by winoground items the results of our more traditional evaluation suggest that the winoground text/image pairs are, even without focusing on semantic minimal pairs, interestingly different from other visuolinguistic datasets. in this section, we seek to characterize what makes the winoground task challenging. see appendix b for details on our annotation method and ## generating non-minimal winoground data with textual variants in this section, we look in depth at whether the minimal textual pairs are simply not sufficiently distingushable with existing vision and language models. that is, at the level of text, does the model not understand that "grass in the mug" is distinguishable from "mug in the grass"? or is the problem instead that the images are not distinguishable-or that the fusion of the visual and linguistic information is too difficult? to tease apart these hypotheses, we run experiments using caption variants: we modify each caption in each winoground item so that the captions are no longer minimally contrastive. we obtain caption variants by using 9 manually selected augmentation strategies from nlaugmenter @xcite and categorize them by the type of modification they make (see we first investigate the separability of textual variants of t 0 from textual variants of t 1 in model embedding space for the three models (lxmert, uniter, clip) in section 5.1. then, we test whether providing models access to textual variants helps performance on the winoground task in section 5.2. finally, we analyze the ability of models to distinguish the right caption conditioned on its textual variant in section 5.3. ## conclusion we initially asked whether failures on winoground occur because sota models rely more on bag-ofwords than they let on and cannot tell the difference between sentences that contain the same words but differ in meaning. we found that the story is more complicated: high lexical overlap between captions is not the only-or even the most likely-cause of failure. first, we showed that it's not only the textual difference between "a mug in some grass" and "a grass in some mug" that makes winoground hard. indeed, using recall@k, we showed that models struggle to identify that either minimally different caption matches a particular image. next, we re-categorized the winoground dataset using a set of tags that identify significant challenges beyond semantic compositionally. for instance, we identified 38/400 items as visuallydifficult, meaning they require identifying a subtle visual feature of the image such as the eye color of a person in an image. performance is very low on this subset, for reasons that may have nothing to do with language. moreover, some of the images (56/400) and captions (50/400) are unusual or hard to parse: these images and captions are challenging for reasons having nothing to do with their inclusion in a minimal pair. even ignoring these cases, we still found that performance on the 171 vanilla winoground items was low. to determine whether this is due to the particular zero-shot evaluation setting used by thrush et al. (2022), we trained small probes to distinguish between lxmert or uniter embeddings of cor-rect matches and incorrect matches. these probes' performance was not consistently better than those trained on a parallel control task, suggesting that zero-shot evaluation is not the source of model failure. so are these examples hard because models do not understand word order? we ran a set of experiments in which we made the textual minimal pairs more different from each other: by augmenting the winoground dataset with variants of each caption, we produced sets of captions which were semantic but not lexical minimal pairs. probing the embeddings of these variants, we found that semantic distinctions were linearly separable from lxmert and uniter layer representations, and the best clip layer representations could be used to correctly distinguish variants of captions not seen during training with over 80% accuracy. even still, all three models fail to match each set of caption variants with the correct image. thus, we observe robust failure on the task even when we use caption variants known to be distinguishable. it seems that the problem is not simply that the model cannot distinguish between captions with overlapping text, but likely lies in associating those distinctions with images. overall, winoground remains a challenging and promising way to test visuolinguistic ability. we would encourage future work to report results on each of the tags we introduce separately, given the clear performance differences across tags we found for clip, uniter, and lxmert. and we urge care in drawing conclusions about the compositional abilities of vision-and-language models.