How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets (2022)

Abstract: A central question in natural language understanding (NLU) research is whether high performance demonstrates the models’ strong reasoning capabilities. We present an extensive series of controlled experiments where pre-trained language models are exposed to data that have undergone specific corruption transformations. These involve removing instances of specific word classes and often lead to non-sensical sentences. Our results show that performance remains high on most GLUE tasks when the models are fine-tuned or tested on corrupted data, suggesting that they leverage other cues for prediction even in non-sensical contexts. Our proposed data transformations can be used to assess the extent to which a specific dataset constitutes a proper testbed for evaluating models’ language understanding capabilities.

## introduction the super-human performance of recent transformer-based pre-trained language models @xcite on natural language understanding (nlu) tasks has raised scepticism regarding the quality of the benchmarks used for evaluation @xcite . there is increasing evidence that these datasets contain annotation artefacts and other statistical irregularities that can be leveraged by machine learning models to perform the tasks @xcite @xcite @xcite @xcite . these studies have so far largely focused on the natural language inference (nli) and textual entailment tasks. the scope of our work is wider, in the sense that we address all but one nlu tasks sentence 1 sentence 2 paraphrase easynews inc. was subpoenaed late last week by the fbi, which was seeking account information related to the uploading of the virus to the isp's usenet news group server. easynews inc. said monday that it was cooperating with the fbi in trying to locate the person who uploaded the virus to a usenet news group hosted by the isp. ## related work annotation artefacts and statistical biases in nli datasets are easily leveraged by the models and can guide prediction @xcite @xcite . examples include explicit negation being indicative of contradiction, and generic nouns suggesting entailment. artefacts are also present in other types of datasets, for example in the roc story dataset where models can provide story endings without looking at the actual stories @xcite . several works have proposed more challenging and cleaner nli datasets where artefacts have been removed @xcite . an efficient way to do this is using adversarial filtering @xcite . the superior quality of the resulting nli datasets is confirmed by @xcite in a series of experiments where it is shown that data corruption affects these higher quality datasets to a greater extent than previous datasets. this work follows the same experimental direction where text perturbations serve to explore the sensitivity of language models to specific phenomena @xcite @xcite . it has been shown, for example, that shuffling word order causes significant performance drops on a wide range of qa tasks @xcite , but that state-of-the-art nlu models are not sensitive to word order @xcite . syntax-based perturbations have also been studied in relation to robustness and faithfulness of machine translation models @xcite . we add to this line of research by applying data corruption transformations that involve removing entire word classes @xcite ) to all but one glue tasks. 1 we interpret high performance of models fine-tuned and/or tested on corrupted datasets as an indication of the presence of lexical cues, and possibly artefacts, guiding prediction, since the meaning of the corrupted utterances is often hard to recover. ## datasets and corruptions in our experiments, we address eight tasks included in the general language understanding evaluation (glue) benchmark for the english language @xcite : cola, mnli, mrpc, qnli, qqp, rte, sst-2, sts-b. following @xcite , we corrupt the training and development sets available for these tasks by removing words of specific word classes. 2 we use the development sets for evaluation, since annotated test data have not been made publicly available. 3 we create three configurations for each task: involves removing all instances of a specific word class from the corresponding dataset (adj, adv, conj, det, noun, num, pron, verb). we label the corrupted datasets by indicating the class of the words that have been removed (e.g., cola-noun, qnli-verb). given the possible combinations of tasks, datasets and corruptions, we end up with 192 setups for our experiments. note that the resulting sentence fragments do not constitute propositions. although not ideal, this is not necessarily problematic for tasks such as sentiment analysis. for inference, the assumption that the task can only be performed at the propositional level is a strong claim, especially given that examples which are not propositions are abundant in existing benchmarks such as mnli (e.g., examples extracted from dialogue). ## models we fine-tune the pre-trained roberta-base model @xcite from the huggingface transformers library @xcite in each of our 192 configurations. we use the same finetuning and evaluation set up for all the experiments. we retrieve the glue datasets using the huggingface datasets library @xcite . we finetune the models for 3 epochs, using a batch size of 32 and a learning rate of 0.00002. ## results the baseline results using the original (noncorrupted) datasets are shown in our results suggest that this is the case also in other glue datasets, such as mrpc and sst-2, where the models still manage to perform fairly well compared to the baseline when fine-tuned on corrupted data. our corrupt-test results in word class dataset accuracy noun corrupt-test 14.7% noun original 34.1% verb corrupt-test 31.1% verb original 66.4% mrpc addresses the paraphrase relationship between sentence pairs. we explore the semantic similarity of the information that remains after corruption. our assumption is that if a sentence pair (from which nouns or verbs have been removed) still contains synonyms or longer paraphrases, this can guide the model towards detecting a similarity or entailment relationship. for this analysis, we use the unigram paraphrases in the l (large) package of ppdb 2.0 @xcite . we find that in the corrupt-test-noun mrpc dataset, 76% of the sentence pairs for which the model made correct predictions still include a lexical paraphrase. sst-2 involves detecting the sentiment expressed in individual sentences. we use the nrclex tool foot_2 to measure the sentiment expressed by lexical cues in the corrupt-test sentences for which model predictions are correct. given that sentiment can be expressed in a text by words pertaining to different grammatical categories, we explore whether lexical cues indicating the polarity of the text still remain after removing instances of a specific word class. in as roberta has been pre-trained using a masked word prediction task, it is reasonable to ask if high model performance with our corrupted datasets could be due to the model's ability to "fill in the gaps" and predict the missing words. to test this, in each sentence of the mrcp development set, we replace the first token that is aimed by a specific corruption procedure (-noun/verb) with the [mask] token. we do this in the original sentence (by removing only the first noun/verb instance) and in the corrupted sentence (where all other nouns/verbs are missing). for example, from the first sentence in ## conclusion we apply a set of controllable corruption transformations to the datasets of nlu tasks in the glue benchmark, and study their impact on model performance. the proposed transformations are generic enough to be applicable to other nlu tasks, and can enrich the available artillery for dataset quality assessment in terms of how efficiently they trigger and test the language understanding capabilities of the models. our results indicate that understanding the meaning of utterances is not required for high performance in most glue tasks. this finding suggests caution in interpreting leaderboard results and in the conclusions that can be drawn regarding the language understanding capabilities of the models. we make our code available foot_3 in order to promote the application of these tests to other nlu datasets, and to favour the development of benchmarks addressing the actual capability of the models to reason about language.