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Title: Simple but effective techniques to reduce dataset biases

Paper content:
There have been several studies recently showing that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models which fail to generalize to out-of-domain datasets, and are likely to perform poorly in real-world scenarios.
We propose several learning strategies to train neural models which are more robust to such biases and transfer better to out-of-domain datasets.
We introduce an additional lightweight bias-only model which learns dataset biases and uses its prediction to adjust the loss of the base model to reduce the biases.
In other words, our methods down-weight the importance of the biased examples, and focus training on hard examples, i.e. examples that cannot be correctly classified by only relying on biases.
Our approaches are model agnostic and simple to implement.
We experiment on large-scale natural language inference and fact verification datasets and their out-of-domain datasets and show that our debiased models significantly improve the robustness in all settings, including gaining 9.76 points on the FEVER symmetric evaluation dataset, 5.45 on the HANS dataset and 4.78 points on the SNLI hard set.
These datasets are specifically designed to assess the robustness of models in the out-of-domain setting where typical biases in the training data do not exist in the evaluation set.
Recent neural models (Devlin et al., 2019; Radford et al., 2018; Chen et al., 2017) have achieved high and even near human-performance on several large-scale natural language understanding benchmarks.
However, it has been demonstrated that neural models tend to rely on existing idiosyncratic biases in the datasets, and leverage superficial correlations between the label and existing shortcuts in the training dataset to perform surprisingly well 1 , without learning the underlying task (Kaushik & Lipton, 2018; Gururangan et al., 2018; Poliak et al., 2018; Schuster et al., 2019; Niven & Kao, 2019; McCoy et al., 2019) .
For instance, natural language inference (NLI) consists of determining whether a hypothesis sentence (There is no teacher in the room) can be inferred from a premise sentence (Kids work at computers with a teacher's help) 2 (Dagan et al., 2006) .
However, recent work has demonstrated that large-scale NLI benchmarks contain annotation artifacts; certain words in the hypothesis are highly indicative of inference class that allow models with poor premise grounding to perform unexpectedly well (Poliak et al., 2018; Gururangan et al., 2018) .
As an example, in some NLI benchmarks, negation words such as "nobody", "no", and "not" in the hypothesis are often highly correlated with the contradiction label.
As a consequence, NLI models do not need to learn the true relationship between the premise and hypothesis and instead can rely on statistical cues, such as learning to link negation words with the contradiction label.
As a result of the existence of such biases, models exploiting statistical shortcuts during training often perform poorly on out-of-domain datasets, especially if they are carefully designed to limit the spurious cues.
To allow proper evaluation, recent studies have tried to create new evaluation datasets that do not contain such biases (Gururangan et al., 2018; Schuster et al., 2019) .
Unfortunately, it is hard to avoid spurious statistical cues in the construction of large-scale benchmarks, and collecting 1 We use biases, heuristic patterns or shortcuts interchangeably.
2 The given sentences are in the contradictory relation and the hypothesis cannot be inferred from the premise.
new datasets is costly (Sharma et al., 2018) .
It is therefore crucial to develop techniques to reduce the reliance on biases during the training of the neural models.
In this paper, we propose several end-to-end debiasing techniques to adjust the cross-entropy loss to reduce the biases learned from datasets, which work by down-weighting the biased examples so that the model focuses on learning hard examples.
Figure 1 illustrates an example of applying our strategy to prevent an NLI model from predicting the labels using existing biases in the hypothesis.
Our strategy involves adding a bias-only branch f B on top of the base model f M during training (In case of NLI, the bias-only model only uses the hypothesis).
We then compute the combination of the two models f C in a way to motivate the base model to learn different strategies than the ones used by the bias-only branch f B .
At the end of the training, we remove the bias-only classifier and use the predictions of the base model.
We propose three main debiasing strategies, detailed in Section 2.2.
In our first two proposed methods, the combination is done with an ensemble method which combines the predictions of the base and the bias-only models.
The training loss of the base model is then computed on the output of this combined model f C .
This has the effect of reducing the loss going from the combined model to the base model for the examples which the bias-only model classifies correctly.
For the third method, the bias-only predictions are used to directly weight the loss of the base model, explicitly modulating the loss depending on the accuracy of the bias-only model.
All strategies work by allowing the base model to focus on learning the hard examples, by preventing it from learning the biased examples.
Our approaches are simple and highly effective.
They require training a simple classifier on top of the base model.
Furthermore, our methods are model agnostic and general enough to be applicable for addressing common biases seen in several datasets in different domains.
We evaluate our models on challenging benchmarks in textual entailment and fact verification.
For entailment, we run extensive experiments on HANS (Heuristic Analysis for NLI Systems) (McCoy et al., 2019) , and hard NLI sets of Stanford Natural Language Inference (SNLI) (Bowman et al., 2015) and MultiNLI (MNLI) (Williams et al., 2018 ) datasets (Gururangan et al., 2018 .
We additionally construct hard MNLI datasets from MNLI development sets to facilitate the out-of-domain evaluation on this dataset 3 .
Furthermore, we evaluate our fact verification models on FEVER Symmetric test set (Schuster et al., 2019) .
The selected datasets are highly challenging and have been carefully designed to be unbiased to allow proper evaluation of the out-of-domain performance of the models.
We show that including our strategies on training baseline models including BERT (Devlin et al., 2019) provide substantial gain on out-of-domain performance in all the experiments.
In summary, we make the following contributions:
1) Proposing several debiasing strategies to train neural models that make them more robust to existing biases in the dataset.
2) An empirical evaluation of the proposed methods on two large-scale NLI benchmarks and obtaining substantial gain on their challenging out-of-domain data, including 5.45 points on HANS and 4.78 points on SNLI hard set.
3) Evaluating our models on fact verification, obtaining 9.76 points gain on FEVER symmetric test set, improving the results of prior work by 4.65 points.
To facilitate future work, we release our datasets and code.
We propose several novel techniques to reduce biases learned by neural models.
We introduce a bias-only model that is designed to capture biases and leverages the existing shortcuts in the datasets to succeed.
Our debiasing strategies then work by adjusting the cross-entropy loss based on the performance of this bias-only model to focus learning on the hard examples and down-weight the importance of the biased examples.
Our proposed debiasing techniques are model agnostic, simple and highly effective.
Extensive experiments show that our methods substantially improve the model robustness to domain-shift, including 9.76 points gain on FEVER symmetric test set, 5.45 on HANS dataset and 4.78 points on SNLI hard set.

assistant: text: We propose several general debiasing strategies to address common biases seen in different datasets and obtain substantial improved out-of-domain performance in all settings.