Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models (2021)

Abstract: Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current COPA models by certain attacks. Previous solutions that tackle the superficial cues of unbalanced token distribution still encounter the same problem of semantic bias, even more seriously due to the utilization of more training data. We mitigate this problem by simply adding a regularization loss and experimental results show that this solution not only improves the model’s generalization ability, but also assists the models to perform more robustly on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect.

## introduction supervised learning algorithms recklessly absorbing all the correlations found in training data is statistically correct but might have missed the point @xcite . hence, recent work has focused more on spurious correlations in datasets in computer vision and nlp @xcite . in inference tasks over natural language, spurious correlation has been identified a lot, such as lexical and * corresponding author grammatical constructs, word overlap, sentence length @xcite , and unbalanced token distribution @xcite . copa @xcite is a natural language understanding task, which requires a system to choose either a cause or effect of a given story event. it is one of the natural language understanding tasks in superglue benchmark @xcite . pretrained language models gain a great improvement on copa, such as bert @xcite , roberta @xcite , and albert @xcite . the recent state-of-the-art model on copa, deberta @xcite , reached a surprising accuracy of 98.4%. however, the complexity of causal reasoning and the requirements of world knowledge imply that the ability of causal reasoning in plms might be overestimated. it is worth exploring whether the models have acquired the ability of causal reasoning. we observe that 66.8% accuracy can be reached by a text semantic similarity model @xcite the main work on exploring bias in copa is from @xcite . they investigate unbalanced token distributions in correct answers in copa training set and show that the good performance brought by bert can be explained by its ability to exploit token distribution in alternatives. they augment the training set with a mirrored-copa set to prevent the models from predicting with token distribution imprudently. however, we observed this improved model relies on semantic bias more seriously than the original plms. we further test the models on a new dataset, bcopa-ce, which evaluates the ability of a system to distinguish the cause and effect and to reason without the clues of token distribution. for alleviating the semantic bias problem, we propose to add a regularization loss to the original objective (section 3). experimental results show that this solution is not only effective in our challenging test set, but improves the generalization ability of the model on the original test set. it also performs more robustly than the original plms in copa-test hard set proposed by @xcite . in sum, our contributions are as follows: ## probing experiments unlike bias about token distribution or sentence length, indirect semantic cues cannot be analyzed statistically. we explore whether plms rely excessively on semantic similarity with special probing experiments. firstly, we observe whether the model has dropped to a great extent if they see a distractive alternative, like a premise. this distractor cannot be the correct answer, but it has a higher similarity score than the correct alternative. moreover, inspired by ## conclusion in this paper, we explore whether copa models rely excessively on semantic similarity for prediction. we add the regularization loss to the training objective to alleviate this weakness. results show that our solution is effective in our adversarial test, and improve the generalization ability and the robustness of models on previous copa-hard dataset. moreover, previous debiased models on token distribution rely on semantic bias more seriously than the original models, which reminds us if debiasing bring more other bias.