Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022)

Abstract: Natural language inference (NLI) is a task to infer the relationship between a premise and a hypothesis (e.g., entailment, neutral, or contradiction), and transformer-based models perform well on current NLI datasets such as MNLI and SNLI. Nevertheless, given the linguistic complexity of the large-scale datasets, it remains controversial whether these models can truly infer the relationship between sentences or they simply guess the answer via shallow heuristics. Here, we introduce a controlled evaluation set called Simple Pair to test the basic sentence inference ability of NLI models using sentences with syntactically simple structures. Three popular transformer-based models, i.e., BERT, RoBERTa, and DeBERTa, are employed. We find that these models fine-tuned on MNLI or SNLI perform very poorly on Simple Pair (< 35.4% accuracy). Further analyses reveal event coreference and compositional binding problems in these models. To improve the model performance, we augment the training set, i.e., MNLI or SNLI, with a few examples constructed based on Simple Pair ( 1% of the size of the original SNLI/MNLI training sets). Models fine-tuned on the augmented training set maintain high performance on MNLI/SNLI and perform very well on Simple Pair (~100% accuracy). Furthermore, the positive performance of the augmented training models can transfer to more complex examples constructed based on sentences from MNLI and SNLI. Taken together, the current work shows that (1) models achieving high accuracy on mainstream large-scale datasets still lack the capacity to draw accurate inferences on simple sentences, and (2) augmenting mainstream datasets with a small number of target simple sentences can effectively improve model performance.

## introduction natural language inference (nli), also known as recognizing textual entailment (rte), is a basic task to test the semantic inference ability of natural language processing (nlp) models @xcite @xcite . the nli task concerns the relationship between a pair of sentences, i.e., a premise and a hypothesis @xcite @xcite . in recent years, a number of datasets have been developed to train models for the nli task, such as stanford nli (snli) @xcite and multi-genre nli (mnli) @xcite , and transformer-based deep neural network models have achieved high accuracy on these datasets @xcite . the high accuracy of nli models could be taken to suggest that these models already have the ability to interpret the meaning of sentences and generate semantic inference. nevertheless, recent evidence shows that nli models may have just guessed the answer based on statistical biases hidden in the datasets @xcite . it also has been shown that models can achieve high accuracy even when the words in premise/hypothesis are shuffled @xcite , casting further doubts on whether the nli models can truly infer the meaning of sentence pairs or simply guess the answer via shallow heuristics @xcite . to understand the true capacity of the current models, one reasonable approach is to generate more complex cases to break the shallow heuristics and accordingly identify the model defects. there is a growing body of recent nli work that constructs syntactically/semantically sophisticated material for nli datasets @xcite @xcite . training and testing models on difficult and challenging material are valuable since this exercise pushes the boundaries of how much nli models can cope with linguistic complexity @xcite . however, the complexity of the datasets could also potentially hinder an explicit picture of what specific linguistic features the models can learn and more importantly what they cannot learn. furthermore, the focus on complex material implicitly assumes that the current nli models have the capacity to understand simple sentences and consequently perform the nli task accurately on simple sentences. in this work, departing from the common practice of constructing complex material, we introduce a controlled evaluation set called simple pair, which includes a large number of syntactically/semantically simple sentences following a set of systematic design features. the goal of the current study is two-fold. first, we ask whether the current nli models have the ability to correctly infer the relationship between simple sentences in simple pair. if not, the failure patterns on these simple cases can more effectively help us identify the basic linguistic operation(s) that the current models fail to capture, and illuminate shortcomings from inappropriate model biases. second, we ask whether the weakness of the models can be overcome using simple training sentences constructed based on simple pair. if so, the seemingly basic linguistic information provided by these simple cases can serve as an important supplement for the existent datasets, and robustly improve the model performance on nli tasks. to preview, we tested three popular transformerbased models, i.e., bert @xcite , roberta @xcite , and deberta @xcite , which were respectively fine-tuned on 2 widely-used datasets, i.e., the mnli and the snli datasets. we found that these models were by and large inaccurate in drawing inference relations on our datasets, indicating severe model problems such as event coreference biases and compositional binding failures. to address these problems, we fine-tuned each model on the mnli or snli augmented with a few samples constructed based on sentences in simple pair. the small number of samples can indeed significantly improve model performance on simple pair, and the positive improvement can extend to more complex and challenging cases. ## related work transformer-based models have achieved humanlevel performance on many nli datasets such as mnli and snli @xcite @xcite . the good performance seems to suggest that these models have the ability to interpret sentences in the current datasets and generate correct inferences. accordingly, follow-up works aim at constructing even more challenging datasets to train and test the models @xcite . there is also a growing body of works that constructs datasets to test more fine-grained linguistically motivated inference patterns such as pragmatic inferences and numerical reasoning @xcite or correlates model errors with well-defined linguistic phenomena @xcite @xcite , with the purpose to identify whether models have trouble making certain types of inferences. compared with these studies, the current work take a different approach: by intentionally reducing the difficulty of the test material, we aim to uncover whether models can truly infer the relation between simple sentences. the results show models perform poorly inferring the relation between basic n-is-a and svo sentences. the current work differs from previous studies in two major aspects. first, we constructed a large set of simple sentences, i.e., simple pair, to test and enhance models. most current datasets are composed of syntactically complicated sentences and it is usually difficult to isolate specific linguistic constructs from these sentences @xcite . in our study, the sentences are simple enough so that the mechanisms to understand (or fail to understand) them are relatively transparent. second, we extended the current mainstream datasets, i.e., mnli and snli, to test the generalization ability of models. in extended pair, the original premisehypothesis pairs in mnli/snli are broken and recombined in a random way. it is an effective method to tackle the issue of potential statistical biases in nli datasets, since most heuristics originating in the original datasets are rendered useless under the new test conditions where all the sentences are unrelated. relatedly, the study of @xcite switched the premise and hypothesis, and used the switched pairs to test nli models. our method can be combined with the method by @xcite to further reduce the inherent statistical biases in nli datasets. many studies have discussed the potential risk of overfitting on benchmark datasets, and emphasized the need to more accurately evaluate the true language capacity of various models @xcite @xcite . for example, it has been shown that models can guess the relationship between a premise and a hypothesis with an accuracy higher than the chance level, even when just considering the hypothesis @xcite . here, by creating premise-hypothesis pairs characterized by neutral relationship, we provide additional evidence that existing models are severely over-fitted: (1) all models tend to judge the relationship between two unrelated simple sentences to be contradictory, which suggests the event coreference bias, and (2) some of them have substantial difficulty solving the compositional binding relations for conjunction sentences. regarding the event coreference bias, many studies have mentioned the event coreference problem in nli tasks @xcite @xcite . consider the sentence pair "a boat sank in the pacific ocean" and "a boat sank in the atlantic ocean" as an example. the pair could be labeled as a contradiction if one assumes that the two sentences refer to the same single event, but could also be reasonably labeled as neutral if they are two independent events. for the snli set, the human annotators were instructed to judge the relation between sentences given that the two sentences describe the same scenario @xcite . hence, sentences that described different entities or events should be considered as contradiction by human annotators. for the mnli set, despite no strict restrictions for a specific scenario between premise and hypothesis in each sample, it is still possible that the annotators adopted a similar annotation strategy in mnli @xcite . therefore, the coreference bias is regarded as an inherent problem in models fine-tuned on snli or mnli, and no studies, to our knowledge, have tried to address this problem. in this work, we show that augmenting snli or mnli with a few samples from simple pair can attenuate the coreference bias in these models. regarding the compositional binding problem, it is surprising that large pre-trained models, e.g., bert and roberta, failed to deal with the fundamental compositional binding relation between a subject and a predicate. it is possible that the compositional failures we observed are also attributed to the inherent biases originating from mnli and snli, given the model performance on conjunction sentences can be significantly improved by the augmented fine-tuning process. ## conclusion in summary, since existing models have shown good performance on large-scale nli datasets, the received wisdom is that these models are capable of doing at least some sophisticated inferences, and more progress can be made by evaluating them on even more challenging and complex datasets. the current work, however, shows that models achieving good performance on large-scale datasets do not necessarily generalize to simpler datasets. in fact, models fine-tuned on mnli or snli generally have lower than chance level performance when inferring the relationship between simple sentences. nevertheless, the results here show that combining a few simple examples with large-scale datasets, e.g., mnli and snli, can significantly increase the model's ability to deal with simple test samples while largely maintaining the performance on origi-nal test samples. the positive results on simple test samples can also robustly transfer to improving the model accuracy on more complex samples. these results indicated that, in addition to more complex material, simple and transparent material, such as simple pair, can also serve as a tool for motivating and measuring progress in nli tasks.