Towards Robustifying NLI Models Against Lexical Dataset Biases (2020)

Abstract: While deep learning models are making fast progress on the task of Natural Language Inference, recent studies have also shown that these models achieve high accuracy by exploiting several dataset biases, and without deep understanding of the language semantics. Using contradiction-word bias and word-overlapping bias as our two bias examples, this paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. Next, we also compare two ways of directly debiasing the model without knowing what the dataset biases are in advance. The first approach aims to remove the label bias at the embedding level. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features by forcing orthogonality between these two sub-models. We performed evaluations on new balanced datasets extracted from the original MNLI dataset as well as the NLI stress tests, and show that the orthogonality approach is better at debiasing the model while maintaining competitive overall accuracy.

## introduction in this work, we focus on investigating and reducing biases in the task of natural language inference (nli), where the target of the model is to classify the relations between a pair of sentences into three categories: entailment, neutral and contradiction. with the release of large-scale standard datasets @xcite , significant success has been made on this task, and recent state-of-the-art neural models have already reached competitive performance even compared to humans. however, a number of papers @xcite @xcite have shown that despite the high accuracy on these datasets, these models are far from mastering the required nature of natural language inference. instead of deeply understanding the sentences in the correct semantic way, these models tend to exploit shortcuts or annotation artifacts in the dataset and actually overfit to these datasets to predict the label using simple patterns. however, most shortcuts are only valid within the datasets and fail to hold for general natural language. hence, these models fail to generalize to other datasets for the same task (talman and chatzikyriakidis, 2019), perform badly on challenge analysis datasets @xcite @xcite , and are fooled by adversarial attacks @xcite . one major cause of this problem is the existence of dataset biases. since most nlp datasets are often collected and processed by crowdworkers, bias can be added to the data at every step of data collection. for example, when writing contradiction pairs, workers are likely to use negation words such as 'not', and when creating entailment pairs, workers are likely to keep most of the words in the premise sentence. this results in 'annotation artifacts' in the dataset @xcite . in reality, almost every dataset contains countless such diverse biases. in our paper, we focus on the multi-genre natural language inference (mnli) dataset @xcite in english, and on two specific kinds of dataset bias: contradiction word bias (cwb): if the hypothesis sentence contains some specific words (such as negation words) that are always used by the crowdworkers to generate contradiction pairs, then the sentence pair is very likely to be contradiction. ## data-level debiasing models naturally learn the biases from the dataset they are trained on. therefore, as we mentioned in q1 in sec. 1, one may first wonder if lexical bias can be completely removed by fixing the source of the bias, i.e., datasets. while collecting largescale datasets @xcite already takes a lot of time and effort, collecting bias-free datasets is even more time-consuming and hard to control. therefore, here we focus on getting additional data from currently-available resources. we conducted experiments using two resources of data. the first one is to do 'data enhancement' by repeating samples in the original training data. the second source is 'data augmentation' by manually creating synthetic data. we follow the construction of existing synthetic bias-revealing datasets to create new samples for the training set so that these targeted biases can be reduced. ## model-level debiasing model-level debiasing methods have the advantage that there is no need to know the specific bias type in advance. here we propose two different methods. the first method focuses on debiasing the content of word/sentence embeddings, where we aim to remove strong bias in the embeddings towards any of the labels so that there will be fewer shortcuts for models to exploit. the second method builds a separate shallow bag-of-words (bow) sub-model and projects the primary model's representation onto the subspace orthogonal to this bow sub-model via the hex projection algorithm @xcite . our proposed methods can be applied to a wide range of baseline model architectures. in addition, none of our methods is bias-type specific, so the results on cwb and wob should generalize to other similar lexical biases. ## conclusion we study the problem of lexical dataset biases using wob and cwb as two examples. we first showed that lexical dataset biases cannot be solved by simple dataset changes and motivate the importance of directly designing model-level changes to solve this problem. for model-level changes, we first show the ineffectiveness of embedding-debiasing approaches, thus highlighting the uniqueness of lexical bias against gender bias problems. next, we robustify the model by forcing orthogonality between a bow sub-model and the main model and demonstrate its effectiveness through several experiments. since none of our methods is biastype specific, we believe these results can also be generalized to other similar lexical biases. finally, we would like to point out that our methods and results here do not mean to belittle the importance of collecting clean/unbiased data. we strongly believe in the importance of unbiased data for model design and evaluation. however, some biases are inherent and inevitable in the natural distribution of the task (e.g., for nli, it is natural that sentences with high overlapping are most likely entailment pairs). therefore, our work stresses that it is also very important to encourage the development of models that are unlikely to exploit these inevitable biases/shortcuts in the dataset. neither model-level debiasing nor data-level debiasing alone is the conclusive solution for this problem. joint efforts are needed for promoting unbiased models that learn true semantics; and we hope our paper can encourage more work towards this important direction. next, in order to create a balanced dataset for these selected contradiction-words, we first select the samples containing these words from the matched development set. in order to let the samples be more difficult and better test the model's bias. we only select the samples where the hypothesis samples contain the contradiction word, while there's no negation word in the premise sentence (so that the contradiction word is generated by the annotator instead of copying from the premise sentence). since the bias of 'not' is not uniformly strong, here we only select samples that both contain 'not' and have small jaccard distance @xcite between the sentence pairs, which we empirically find that the bias is stronger. after selecting these samples, we can extract a testing set with most of the samples labeled as contradiction, but the label distribution is severely unbalanced. in order to balance the label distribution, we randomly sample some examples from the training set using the same criterion (containing contradiction word in the hypothesis sentence but no negation word in the premise sentence) and put them in the testing set. our resulting dataset contains 1100 samples with 550 are labeled as contradiction and the other 550 are non-contradiction labels. since the domain of the training set is different from the domain of the mismatched validation set, we only extract a balanced test set based on the matched validation set.