Debias NLU Datasets via Training-free Perturbations
Authors: Qi Guo
yuanhang tang
Yawen Ouyang
Zhen Wu
Xinyu Dai
Venue: EMNLP 2023 Findings (2023)
Keywords: natural language understanding; out-of-distribution generalization; data-centric debiasing;
TL;DR: This work proposes a cost-effective dataset debiasing framework based on a training-free perturbation strategy for NLU datasets.

Abstract: Several recent studies have shown that advanced models for natural language understanding (NLU) are prone to capture biased features that are independent of the task but spuriously correlated to labels. Such models often perform well on in-distribution (ID) datasets but fail to generalize to out-of-distribution (OOD) datasets. Existing solutions can be separated into two orthogonal approaches: model-centric methods and data-centric methods. Model-centric methods improve OOD performance at the expense of ID performance. Data-centric strategies usually boost both of them via data-level manipulations such as generative data augmentation. However, the high cost of fine-tuning a generator to produce valid samples limits the potential of such approaches. To address this issue, we propose PDD, a framework that conducts training-free Perturbations on samples containing biased features to Debias NLU Datasets. PDD works by iteratively conducting perturbations via pre-trained mask language models (MLM). PDD exhibits the advantage of low cost by adopting a training-free 
perturbation strategy and further improves the label consistency by utilizing label information during perturbations.  Extensive experiments demonstrate that PDD shows competitive performance with previous state-of-the-art debiasing strategies. When combined with the model-centric debiasing methods, PDD establishes a new state-of-the-art.

Debias NLU Datasets via Training-free Perturbations
Qi Guo Yuanhang Tang* Yawen Ouyang* Zhen Wu Xinyu Dai
National Key Laboratory for Novel Software Technology, Nanjing University
Institute for AI Industry Research (AIR), Tsinghua University
edu.cn ouyangyawenéair. tsinghua. {qguo, tangyuanhang wjw.edu.cc..
{wuz, daixinyujenju.edu cn
Abstract
are commonly characterized as spurious correla- tions between task-independent features and labels (Gardner et al., 2021). These lusk-independent features exhibiting such spurious correlations are viewed as biased features. Models relying on these biased features for inference usually produce su- perior in-distribution (ID) performance but fail to generalize to -ut-of-distribution (OOD) datasets. For instance, in the MultiNLI (MNLI) dataset (Williams et al., 2018), negation words are highly correlated with the contradiction label and consid- ered as biased features (Joshi et al., 2022: Shah et al., 2020). Models trained on the MNLJ dataset tend to arbitrarily classify pairs containing negation words into the contradiction category.
Several recent studies have shown that ad- vanced models for natural language understand- ing (NLU) are prone to capture biased features that are independent of the task but spuriously correlated to labels. Such models often per- form well in-distribution (ID) datasets but fail to generalize to out of-distribution (OOD) datasets. Existing solutions can be separated into two orthogonal approaches: model-centric methods and data-centric methods. Model- centric methods improve OOD performance the expense of ID performance. Data-centric strategies usually boost both of them via data- level manipulations such generative data augmentation. However, the high cost of fine- tuning a generator to produce valid samples lim- its the potential of such approaches. To address this issue, we propose PDD, a framework that conducts training-fiee free Perturbations on sam- ples containing biased features to Debias NLU Datasets. PDD works by iteratively conducting perturbations via pre-trained mask language models (MLM). PDD exhibits the advantage low cost by adopting a training- free per- turbation strategy and further improves the la- bel consistency by utilizing label information during perturbations. Extensive experiments demonstrate that PDD shows competitive per- formance with previous state-of-thed the---- debias- ing strategies. When combined with the model- centric debiasing methods, PDD establishes new state-of-the-aae
Previous solutions to resolve the issue can be roughly separated into two types: model-centric methods and data-centric methods (Wu al., 2022). Model-centric methods boost the robustness of models by designing new training objectives or model architectures to force the models into pay- ing less attention to the biases during training, at the expense of ID performance (Karimi Mahabadi et al., 2020; Clark et al., 2019). Data-centric meth- ods conduct data-level manipulations to debias the original datasets (Ross et al., 2022; Wu et al., 2022; Yang et al., 2020). Recently, data-centric debiasing methods, especially those through generative aug- mentations, are gaining increasing attention due to their ability to enhance both ID and OOD perfor- mance simultaneously.
However, existing data-centric methods based on generative augmentations are still faced with the limitation of high cost (Ross et al., 2022). These methods usually take effort to finetune a large task-specific generator, such as GPT-2 (Radford et al., 2019), on large-scale NLU datasets to generate new samples. Additionally, they inevitably require costly retraining once the dataset varies, further increasing the computational costs.
1 Introduction
Although recent language models have demon- strated impressive performance on many natural language understanding (NLU) benchmarks (Wang et al., 2018), several studies show that models tend to leverage dataset biases for inference (Po- liak et al., 2018; Zhang et al., 2019). Such biases
"Corresponding author.

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To address the limitation of existing data-centric debiasing methods, we propose PDD (Perturbing samples containing biased features to Debias NLU Datasets), a novel and cost-effective debiasing framework for NLU datasets based on a training- free perturbation strategy. To reduce expenses, we adopt a pre-trained mask language model (MLM) to perturb samples rather than finetuning a gen- erator generate new samples. Specifically,, perturb the samples containing biased features to mitigate the strong statistical correlations between such features and the labels, which is measured by z-statistics (Gardner et al., 2021). Moreover, we design label-specific prompts to incorporate label information into perturbations and further adopt confidence filter step to improve label consistency. To verify the effectiveness of PDD, carry out experiments on three NLU tasks: natural language inference (Bowman et al., 2015), fact verification (DAGAN et al., 2009), and para- phrase identification. Experimental results demon- strate that PDD shows comparable OOD perfor- mance to previous ofte-offthhe-rt debiasing meth- ods. Since PDD is data-centric, we enhance it with model-centric methods and observe fur- ther improvements in performance, exceeding any previous state-of-the-an the best of our knowledge. Our code and data are available at: PDD. In summary, our contributions include:
et al. (2021) formalize spurious correlations in causal framework and consider the model's prediction should be invariant to perturbations of the spurious feature. Gardner et al. (2021) assume a uniform distribution obeyed by the prediction conditioned on any single feature and figure out spurious features with hypothesis testing.
Model-centric strategies aim prevent models from overfitting the biased features in samples during training. Belinkov et al. (2019b) improves the robustness of models on OOD datasets through the removal of biased features at a representative level with adversarial training techniques. Product-of-Expeet (Karimi Mahabadi et al., 2020) and its variant Learned-Mixin (Clark et al., 2019) debias the model by adjusting the loss function to down- weight the samples that can be casily solved by bias-only models. Tang et al. (2023) capture the model biases automatically by shuffling the words of the input sample and further debias the models in product of experts. Utama et al. (2020) introduce a confidence regularization method to
Model-centric Methods:
discourage models from exploiting biases, without harming the ID performance. Yaghoobzadeh et al. (2021) robustify models by fine-tuning the models twice, first on the full data and second on the minorities with low confidence.
Data-Centric Methods: Several works try to improve the robustness by conducting data-level manipulations such data augmentations and data filtering. Bartolo al. (2021) generate adversarial datasets improve the robustness of question answering models. Le Bras et al. (2020) propose AFLITE which adjusts the distribution through adversarial filtering. Wu et al. (2022) fine-tunes a GPT-2 (Radford et al., 2019) to fit the original dataset, then conducts spurious filtering to generate debiased dataset. Ross et al. (2022) alleviate the syntactic biases in datasets by semantically-controlll perturbations generate samples with high semantic diversity. Different from these approaches, PDD generates samples via a training-free perturbation strategy and can be applied to a variety of known dataset biases.
We introduce PDD, an effective and low- cost dataset debiasing framework based training-fin frine perturbation strategy, compensat- ing for the drawbacks of existing data-centric debiasing methods.
We conduct extensive experiments on several NLU benchmarks and demonstrate that our framework outperforms strong baselines on several ODD datasets.
2 Related Work
Biases in Datasets: Biases in NLU benchmarks are idiosyncratic artifacts introduced in the annotation processes and are often modeled as spurious correlations between simple features and labels. Several works trying provide theoretical framework to measure such spurious correlations. Gururangan et al. (2018) involves pointwise mutual information (PMI) to quantify the spurious correlations in datasets. Veitch
3 Methodology
In this section, we describe how our framework PDD works in detail. We start by selecting a set

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Before
After
x
p(ylx)
S
X
Perturb
1r
X
S₂
Perturb
z-value
z-value
33
x
Perturb
W
X
x
Biased feature
Entailment
Acceptance region
Sample containing
Neutral
Add sample dataset
Sample not containing
Contradiction
Remove sample from dataset
Figure 1: An overview of PDD. The z-value of is rejected by the uniform hypothesis (above the threshold). To reduce its z-value, we iterate through the samples that contain æ and conduct perturbations to generate new samples to make á closer to Pu- Each the z-values over all labels are under the threshold.
sample perturbed, we update the and z-value. The process stopped when
of bask-independent features based on prior knowl- edge (Poliak et al., 2018; McCoy et al., 2019) and quantify their correlations to the labels via z-value so as to identify the biased features (section 3.1).
to utilize hypothesis testing technique. The as- sumption behind that the posterior probability given only a task-independent feature should fol- low uniform distribution. And the features vi- olating the assumption are considered as biased features. More formally, for each task-independent feature æ, let N be the number of samples contain- ing x. K the number of classes, and po 1/ K the ideal probability following the uniform distri- bution. The conditional probability p(u = l|x). which is approximated by its expectation value p(y = 1|x) = 1/2 ا T(w = 1), should be uni- form over the class labels I. Under the uniform hypothesis: ~ 1| Po. we compute the standardized z-statistics, which measures the devi- ation of from the uniform distribution:
Then we iteratively conduct perturbations on the samples containing biased features to reduce the z-value and produce debiased datasets (section 3.2).
The key component training-free perturbation strategy - described separately (section 3.3).
Throughout this section, we use the NLI task as example for illustration. Note that frame- work not limited to the NLI task but is applicable wide range of NLU tasks.
3.1 Quantifying Dataset Biases
We start by selecting a set of task-independent fea- tures that widely studied in previous works (Gu- rurangan et al., 2018; McCoy et al., 2019; Poliak al., 2018). Specifically, we select the following three features: (1) unigrams: (2) lexical overlap (the number of words shared by premise and hy- pothesis normalized by the length of hypothesis); (3) hypothesis-ong predictions (the predictions of model that takes only the hypothesis the input). To identify biased features and quantify the dataset biases, we follow Gardner et al. (2021)
p - Po
=1,1xx = Vpo(1 Po)/n
(1)
We refer to the absolute value of z-statistic z- value. The larger the z-value is, the stronger the correlation between r and We consider the fea- tures whose z-value exceeds a predefined threshold as the biased features and collect them as a biased feature set Xb.-

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3.2 Debiasing Datasets by Reducing Z-value Under the uniform hypothesis, debiasing a dataset is equivalent to weakening the correlation between the iask-indepnndent features and the labels un- til their z-values are all within the threshold. To achieve this, we iteratively perturb the samples that contain biased features reduce the z-value.
Algorithm 1: Z.vvluu Reducing
Input: Biased dataset Db; Biased feature set X6: Number of labels K; Threshold
Output: Debiased dataset D.
Initialize D+-bo-
for x E Xu
Compute x" (I, (1 -, 1,..,
while max (|2 (1, > do
for S = (P. H, E Db do if S contains a and [aad|2" (1,a)) > then
Let D3 denote the original biased dataset, and the debiased dataset. we initialize the debiased dataset D = Dg. For each x с Xs, we compute the valuue of x over all labels. Then we iter- ate through the samples (P, H.I) that contain x and conduct perturbations on those samples with z-value exceeding the threshold т. Those samples to be perturbed can be separated into two cases: (I) ply- 1[:)) Po and (2) p(y 1|x=) > Po- For each sample in the first case where p(y (1)) < po. we perturb it to generate a new sam- ple S' = (P', II',, , D. so as to increase p.. For each sample in the sec- ond case where p = [[x) > po, we perturb it to get S" - (P', H" 1) not containing and add it to D and remove the original sample S from D, as decrease p. Both the two operations above aim reduce the z-value by bringing the Ç(y 1|x)) closer to po (see appendix C for proof). The pertur- bations are conducted iteratively until the z-values of all x over all labels are under the threshold т. The perturbation strategies are detailed in the next section and the full algorithm is demonstrated in Figure and Algorithm 1.
if ply = [|x) < Po then
Perturb S get
8' (P', H',
containing x
Add S to D
end else
Perturb S to get S' (P', H",
not containing i Add S' to D Remove S from D
end
Update =' (1,x))
still containing æ and add it to
end
end
end
D D
end
should be retained when masking words. To be specific, for unigrams, we skip the biased words and randomly mask the rest of the words to a fixed percentage. For lexical overlap, we retain the over- lapped words in the premise and the hypothesis and randomly mask the rest. For hypothesis-only predictions, we retain the hypothesis and randomly mask the premise.
3.3 Training-free Perturbations
p = 1|x) > po In this case, we generate new samples not containing x to decrease poy ([>) to bring closer to po- Therefore, x should be eliminated in the masking stage. Specifically, for unigrams, we mask the biased word and the rest randomly to fixed percentage. For lexical overlap, we mask the overlapped words and the rest to a fixed percentage. In terms of the hypothesis-only predictions, we randomly mask the hypothesis.
In order to perturb the samples to generate new samples at low cost, we abandon the previous way to fine-tune large generator and instead pro- pose training-free perturbation strategy consisting of two stages: (1) fcature-speeific masking and (2) label-preserving augmentation via MLM. The first stage regulates the appearance of biased features in the generated samples by masking tokens and the second stage helps generate samples while pre- serving the original labels. The whole procedure is depicted in Figure 2.
3.3.2 Label-preserving Augmentation via MLM
3.3.1 Feature-specific Masking
After masking, we employ MLM to fill in the blanks to generate samples. To improve label consistency, we propose prompt-based filling encourage the MLM to preserve the labels and a confidence filtering step to filter out the generated samples with inconsistent labels.
Since the biased features vary, manually design different masking rules for different biased features according to the value of و (listed in appendix A.
5 = 1|x:) < Po In this case, we gen- crate new samples still containing x to increase p(y = 1|x)) so as to bring closer to Pp- Therefore,

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P: He sleeping. Nobody sleeps.
awake. Hi He sleeps.
Everyone stays. H:l Nobody leaves.
P: Nobody stays, Nobody remains.
E
Filter
Filter
MASK
MASK
MLM
MLM
<mask>. H: <mask> sleeps.
He is <mask>,
No
<mask> sleeps.
P: <mask> stays. Nobody <mask>.
<mskk> stays.
Yes
Nobody <mask>.
C
E
Prompt
Prompt
(1) (C | Nobody) > Po
(2) P(E Nobody) <
Po
Figure 2: An illustration of our perturbations in NLJ Each sample consists of a premise (P) and hypothesis (H). E, N, and C represent entailment, neutral, and contradiction respectively. The biased word "Nobody" is highly correlated with the label and rejected by the uniform hypothesis testing. In the case & > Po (left), we mask "nobody" and adopt prompt-based filling generate batch of candidates. Then adopt confidence filter to select the with the highest confidence on the correct label. In the case p < (right), we preserve "nobody" during masking, followed by a similar subsequent augmentation process.
Prompt-Based Filling: To improve label consistency of generated samples, we design label-specific prompts to incorporate the label information into the input of the MLM. In this way, the MLM is encouraged take the label informa- tion into consideration. To build such prompts, we follow Zhou et al. (2022) to map the label to a relevant prompting word [wo]. Specifically, We map "entailment" to "Yes", "neutral" to "Maybe", and "contradiction" to "No". We feed the connected input "P "Pmmate? [w]] - Hmask" into the MLM, where Prmask denotes the masked premise and Hmask denotes the masked hypothesis, encourage it to generate samples with correct labels.
inference, fact verification, and paraphrase identi- fication. We compare PDD with existing state-of- the-art debiasing strategies several OOD chal- lenging datasets (Karimi Mahabadi al., 2020; Clark al., 2019; Utama et al., 2020; Meissner et al., 2022; Wu et al., 2022; Sanh al., 2021).
4.1 Natural Language Inference
Following previous works (Poliak al., 2018; McCoy al., 2019), we use the MultiNLI (MNLI) (Williams et al., 2018) as the original dataset. MNLI has two ID test sets (MNLI-m and MNLI-mm), one that matches the domains of the training data and one with mismatching domains.
Source Dataset:
Though label-specific prompts help to preserve the labels, the MLM may still generate samples that are inconsistent with the labels. To address this, we follow previous works (Yang et al., 2020; Wu et al., 2022) to train BERT-base model the original dataset and use it as a filter. For each masked sample, various candidates and feed them to the filter. We choose the one with the highest output confidence to maintain label consistency.
Confidence Filtering:
Biases in Dataset: (1) syntactic bias: Mc- Coy al. (2019) find that models tend exploit syntactic biases in datasets (e.g.. the syntactic overlapping between the premise and hypothesis is strongly correlated with the entailment label). (2) hypothesis-only bias: Poliak et al. (2018) shows that a hypothesis-only model that can only see the hypothesis during training captures statistical cues in the hypothesis and succeed in predicting the majority of the test samples accurately.
generate
4 Experimental Setup
To validate the effectiveness of PDD, we conduct experiments on three NLU tasks: natural language
Debiased Dataset:
Considering the biases in MNLI, we select three features: (1) unigrams;

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(2) lexical overlap > 0.8; (3) hypothesis-only predictions and debias them one by one. We first debias the hypothesis-only prediction, then the lexical overlap, and finally the unigrams. The order is determined by ranking the z-value from highest to lowest. Details of our debiased datasets are shown in appendix A.
4.3 Paraphrase Identification
Source Dataset: This task is determine whether two given questions are semantically matching (duplicate) or not. We adopt the Quora Question Pairs (QQP) (DataCanary, 2017) dataset for debiasing. QQP consists of pairs of questions which are labeled duplicate or non-duplicate.
OOD Evaluation Dataset: For syntactic bias, we adopt HANS (McCoy al., 2019), challenging evaluation dataset where the lexical heuristics in MNLI fails. For hypothesis-only bias, we choose the MNLI-hard (Belinkov et al., 2019a) that is constructed from the two ID test datasets by filtering out the samples that can be accurately predicted by hypothesis-only model.
Biases in Dataset: Syntactic bias: In QQP, pairs with low word overlap (<0.8) are heavily unbalanced over labels, indicating severe lexical overlap bias in QQP. Models trained on QQP heavily rely on lexical-overlap fcatures for inference and fail on negative question pairs with high word overlap (Zhang et al., 2019).
Debiased Dataset: We consider two fea- tures: (1) lexical overlap < 0.8 and (2) unigrams, and debias them in descending order of z-value, Notice that samples in QQP with low lexical overlap are heavily biased to the non-duplicate label. Hence we choose lexical overlap < 0.8 instead of lexical overlap 0.8.
4.2 Fact Verification
This task involves verifying claim sentence in the context of given evidence sentence. We use the FEVER dataset (Aly al., 2021) that consists of pairs of claims and evidence generated from Wikipedia. FEVER consists of samples with three labels: support, refutes, and not enough information.
Source Dataset:
OOD Evaluation Dataset: We select the QQP subset of PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang al., 2019) for evaluation. Paws contains 108.463 pairs where the majority of non-paraphrase pairs have high lexical overlap. Models trained on QQP fail miserably on PAWS, much worse than the random baseline.
Biases in Dataset: Claim-only bias: Schus- ter et al. (2019) find that in FEVER the occurrence of words and phrases in the claim is biased toward certain labels, which is exploited by claim-only models for inference.
4.4 Compared Methods
Debiased Dataset: We select two features (1) unigrams and (2) claim-only predictions, and debias the claim-only predictions first then the unigrams. The order is determined by ranking the z-value from highest to lowest. Note that samples with "not enough information" labels just account for fairly small proportion of the whole training set, which makes it difficult reduce the z-value, address the difficulty, we view "refutes" and "not enough information" as one class when computing v-values.
For a comprehensive evaluation, we compare PDD against range of existing debiasing methods.
BERT-base (baseline) (Devlin et al., 2019) which shows impressive performance on dif- ferent NLU benchmarks (Wang al., 2018). is considered as the base model.
Product-of-Expert (PoE) (Karimi Mahabadi et al., 2020) ensembles a bias-only prediction with the prediction of the main model to en- force the main model to focus on the samples that not predicted correctly by the bias- only model.
OOD Evaluation Dataset: For claim-only bias, use the Fever-symmetric dataset (Schuster al., 2019) for evaluation. This challenging set is constructed by making claim and evidence appear with each label to avoid the idiosyncrasies in claims. Hence, models relying on biases in claims fail on the challenging OOD datasets.
Learned-Mixin (Clark al., 2019) improves the performance by proposing a Icarnable weight for the bias-only model's predictions.

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MNLI-m
MNLI-mm
Method
ID
OOD
ID
OOD
dev
test
dev-hard
test-hard
dev
Test
dev-hard
test-hard
BERT-base (baseline)
84.06
84.69
76.42
76.21
84.65
83.49
77.71
75.68
PoE (Karimi Mahabadi et al.. 2020)
84,6
84.1
78.2
77.5
84.9
83.5
79.1
78.3
Learned-Mixin (Clark et al., 2019)
80.5
79.2
81.2
80.4
Reg-conf (Utama et al., 2020)4
84,6
84.1
78.3
85.0
84.2
77.3
Z-aug (Wu al., 2
84.72
85.12
78.95
85.14
84.09
80.29
78.51
PDD (ours)
84,81
85.09
79.12
78.67
84.95
83.73
80.06
78.27
Poll MNLI (baseline)
81.84
82.32
78.19
78.23
82.31
81.29
79.58
77.01
PoR 7-aug
82.39
83.47
79.86
79.84
82.35
82.15
81.12
78.92
PoH PDD (ours)
83.14
83.72
79.88
79.79
83.28
82.42
81.06
78.95
Table 1: Accuracy of models on the in-distribution datasets of MNLI-m and MNLI-mm along with their hard datasets: MNLI-m hard and MNLI-mm hard. We present the best results bold and the second with underlines. and represent the model-centric methods and data-centric methods respectively. are results reported in previous works.
Reg-conf (Utama et al., 2020) utilizes confi- dence regularisation avoid the drop in ID performance and simultancously improves the OOD performance.
for PDD, adopt XLM-large (Conneau et al., 2019) the MLM and train the BERT-base model on the debiased dataset produced by PDD. Experi- ments are repeated times with different random seeds and the average scores are reported. More implementation details are listed in appendix B.
Reweighting (Schuster al., 2019) assigns balancing weight to each training sample based on the correlation of the n-grams within the claims with labels for FEVER.
Results
Experimental results on MNLI-hard, HANS, FEVER and QQP are shown in Table I, Table 2, Table 3, and Table 4 respectively. The main results are summarized as follows.
SettaasinnMaskiing (Meissner et al., 2022) assumes that bias in NLI is caused by a certain subset of weights in the network and designs mask search to remove the biased weights.
PDD outperforms the BERT baseline on both ID and OOD datasets. Compared with the BERT-base model trained on the original dataset, PDD greatly boosts the OOD accuracy by 5.7 on HANS, 2.46% on MNLI-m hard, 2.59% on MNLI- mm, 4.43% on syymerriiii and 6.49% on PAWS respectively. Additionally, PDD also shows better ID performance than the bascline on these benchmarks except QQP. The substantial ID im- provements FEVER (85.66-88.51) may be at- tributed to the distributional gap between the train- ing and the development set.
Weak-Learner (Sanh et al., 2021) leverages limited capacity models which learn to exploit biases to train more robust model via PoE.
Z-aug (Wu et al., 2022), typical data-centric debiasing method for NLI datasets, augments samples via a fine-tuned GPT-2 with a subse- quent z-filtering step.
Enhaneing PDD with Model-Centrie Methods: Since PDD data-centric debiasing method and is agnostic to the models, we examine whether model-centric debiasing methods can also benefit from our debiased datasets and further improve the robustness of models. Following (Wu et al., 2022), we experiment PoE on our debiased MNLI dataset and evaluate MNLI and HANS (implementation details listed in appendix D).
PDD demonstrates competitive perfor- mance with existing state-of-the-art methods. On MNLI-hard and HANS, PDD obtains similar improvements to model-centric debiasing methods. On FEVER, we notice that PDD achieves the best both the development set (88.51) and the symmetric set (62.48).
PDD yields further improvements when combined with model-centric methods. After conducting PoE on our debiased dataset, we find that models exceed any single debiasing method
4.5 Implementation
We adopt the BERT-base model (Devlin et al., 2019) as the backbone for all the methods. As

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and establish new state-of-the-art on HANS to the best of our knowledge.
1.79%, 1.38%, 0.59% for Roberta-base, Roberta- large and Albert-xxlarge respectively. Additionally, great improvements in aceuracy are observed on the HANS dataset. One possible reason is that the feature-specific masking helps to relieve the syntac- tic heuristic by directly masking the words shared between the premise and the hypothesis.
Method
HANS
BERT-hase (baselinc)
63.25
PoH(Karimi Mahabadi ct al., 2020)
66.31
Leamed-Mixin (Clark et 2019)
66.15
Reg-conf (Utama et al., 2020)
69.1
Reg-confyelf-deeeee debbias (Utama al., 2020)
67.1
Weak-L.earnee (Sanh et al., 2
67.9
Model
MNLI
PDD
A
Debiasing-Masken Masking (Meissner al., 2022)
68.69
On MNLI-m hard
Z-aug (Wu et al., 2022) PDDD (ours)
67.69
Roberta-base
81.52
82.20
+0.68
68.96
PoΣ+ MNLI (baseline)
68.84
Roberta-large 85.24
86.21
+0.97
PoE Z-aug
72.04
Albert-xxlarge 85.74
86.27
+0.53
PoE PDD (ours)
72.86
On MNLI-mm hard
Roherta-base 81.92
82.83
+0.91
Table 2: The accuracy of models evaluated on HANS.
Roberta-large
85.21
85.56
+0.35
Albert-xxlarge
86.13
85.98
-0.15
On HANS
Method
dev
symmetric
Roberta-base
73.86
77.65
+3.79
BERT-basc (baseline)
85.66
58.05
PoB (Karimi Mahahadi 2020)*
83.3
62.0
Roberta-large
77.48
80.31
+2.83
Leamed-Mixinn (Clark al. 2019)*
83.1
60.4
Albert-xxlarge
76.31
77.71
+1.40
Reg-conf (Utama et al., 2
86.4
60.5
Reg-conf felf dedina (Utama et al., 2020)**
87.6
60.2
Reweighting (Schuster et al., 2019)*
85.5
61.7
Table 5: Performance of larger PLMs on MNLI.
Weak-Leuner (Sanh ct al., 1
86.4
58.5
PDD (ours)
88,51
62.48
Table 3: The accuracy of the models on the development set FEVER and FEVER FEVVeR-symmetric.
6.2 Debiasing Augmentation vs Common Data Augmentation
To verify whether the improvement robustness on OOD datasets results from our perturbations or just from the increment of data size, we contrast our method with two common augmentation strategies:
Method
dev
PAWS
BERT-base (buseline)
91.27
35.82
PoF (Karimi Mahahadi er al., 2020)*
86.9 9
56.5
Leamed-Mixin& (Clark al., 2019)*
87.6
55.7
Reg-conf (Utama et al., 2020)*
89.1
40.0
Easy Data Augmentation (EDA) (Wei and Zou,
Reg-conf.eef-debbaa (Utama et al., 2020)*
89.0
43.0
2019) and Back Translation (BT) (Fadaee et al.,
Reweighting e (Schuster et ul., 2019)*
89.5
48.6
2017; Sennrich et al., 2016; Yu et al., 2018).
Debiasing-Muskingg (Meissner al., 2
89.6
44.3
PDD (ours)
89.37
42.31
Figure 3 shows the results. The increment of data size does not necessarily lead to better OOD performance and the improvement of ro- bustness mainly results from our perturbations. PDD achieves higher accuracy on both MNLI-hard and HANS, suggesting the models actually benefit from our perturbations.
Table The accuracy of the models on the development set of QQP and PAWS.
6 Discussions and Analysis
6.1 Generalisation Larger PLMs
While the back translations help improve the OOD accuracy, EDA harms it. Additionally, we exploit the potential of PDD as regular augmenta- tion framework by setting the threshold of z-valuc to 0 to produce more samples and finally get de- biased dataset with 740K samples. We find slight performance decrement after augmentation. One possible explanation is that the increment of data size introduces unknown biases.
We verify whether PDD is able to generalize to larger and more powerful Pre-trained Language Models (PLMs). We select three widely-used PLMs substitute for Bert-base model: Roberta- base, Roberta-large (Zhuang et al., 2021), and Alberta-xxlarge (Lan et al., 2019).
As observed in Table 5, PDD successfully gen- eralizes to larger PLMs, yielding average gain of

---

6.4 Ablation Study
85
80
We perform ablation studies to assess the impact of different components in our framework including (1) biased features and (2) the confidence filter.
Accuracy
63.25 64.7
Results on MNLI-m and HANS are presented in Table 7. All the components contribute to the improvement of model robustness. A Performance drop is observed when any of the selected components is removed. Even the removal of hypothesis-only prediction which only targets the hypothesis-only bias influences the performance of models on HANS, indicating that those features are not independent and all contribute to the robustness of models.
MNLIm hard
HANS
Figure 3: Accuracy of models trained on different aug- mented datasets on MNLI-m hard (left) and HANS (right).
6.3 Transfer Performance
Following Karimi Mahabadi al. (2020), we eval- uate how well PDD can generalize to multiple out- of-domain test sets. We select 7 different datasets including MPE (Lai et al., 2017), MRPC (el Said et al., 2015), SCITAIL (Khot et al., 2018), SICK (Marelli et al., 2014), QNLI, WNLI, and RTE (Wang et al., 2018). We take the Bert-base models trained on debiased MNLI and test them on the development sets of these datasets.
Conclusions
We propose PDD, a novel and cost-effective de- biasing framework for NLU datasets adopting a training-free perturbation strategy to mitigate the statistical correlations between biased features and labels. In contrast with existing data-centric strate- gies, PDD shows applicability to various biases in different datasets at a low cost. Extensive ex- periments demonstrate its effectiveness over exist- ing baselines. When combined with model-centric methods, PDD further improves the OOD perfor- mance, achieving new state-of-the-art. Future work may concern extending PDD with automatic identifications of biased features.
PDD achieves substantial improvement in generalization to multiple OOD datasets (Table 6). Models trained on debiased MNLI achieve an average gain of 1.24% on accuracy and perform better than the baseline on 6 of these datasets.
Dataset MPE
Baseline
PDD
75.07
76.68
+1.61
MRPC
57.84
59.31
+1.47
QNLI
50.61
50.86
+0.25
Limitations
WNLI
43.66
45.07
+1.41
SCITAIL
76.99
77.99
+1.00
Despite that PDD achieves impressive results on several challenging OOD datasets, still has the following limitations: (1) The selection of biased features heavily relies on prior knowledge. (2) Since our algorithm to reduce the z-value is a greedy one, the operation mitigate the z-value of one class may influence the z-values of other classes. is difficult balance the z- z-value over all classes for tasks with a large size of labels.
SICK
55.13
54.07
-1.06
RTE 74.37
78.34
+3.97
Table 6: Accuracy of models trained debiased MNLI transferring to new target datasets.
Method
MNI.I-m hard
HANS
Baseline
76.42
Acknowledgements
Full method
79.12
68.96
w/o unigrams
78.56
67.58 1.38
We would like to thank the anonymous reviewers for their constructive comments. This work supported by the National Natural Science Founda- tion of China Projects (No. 62206120 12, 61936012 and 61976114
w/o lexical overlap
78,80 0.32
66.49 2.47
w/o hyp-only prediction
76.70 22.22
64.52 4.44
w/o confidence filtering
74.87
56.96
Table 7: Ablation study conducted on MNL.-m hard and HANS.

---

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---

A Debiased datasets
A.3 Debiased FEVER
Prediction =
Prediction = or N
A.1
Data Size of Debiased Datasets
1.0
0.8
0.6
0.6
0.4
Dataset Original
Debiased
0.2
MNLI 391,120
594,220 284,767
0.0
5
R.or
0.0
or N
FEVER
242,911
QQP
384,346
571,706
Figure 7: The label distribution of samples under differ- ent predictions of claim-only model in FEVER.
Table 8: Data size of our debiased datasets.
Prediction
Prediction
A.2
Debiased MNLI
0.8
0.8
0//08
0.6
0.4
0.4
0.2
Lexical everlap > 0.8
Lesical overlap >
0.0
0.0
1.0
100
Figure 8: The label distribution of samples under dif- ferent predictions of claim-only model in debiased FEVER.
0.6
0.6
0.2
0.2
A.4 Debiased QQP
Figure 4: The label distribution of samples with lexical MNLI (left) and debiased MNLI (right).
overlap >
H-nny predictio...
H-nnly prediction...
Lexical overlap 0.8
Lexical everlap 0.8
1.0
1.0
1.0
1.0
1.0
0.8
0.0
0.8
0.8
0.6
0.6
0.6
0.0
0.4
ouplicate
non dupicctee
0.0
duplicate
non duplicate
Figure 5: The label distribution of samples under differ- ent predictions of hypothesis-only model in MNLI.
Figure 9: The label distribution of samples with lexical overlap < 0.8 in QQP (left) and debiased QQP (right).
Harrly pradiction
Hoarly preckttion
Honry prediction
A.5 Masking Rules
0.4
0.4
0.2
0.2
0.2
We manually design the masking rules for different biased features under two situations I > Po and Po- All masking rules derive from a random masking method and are listed in table 9.
0.0
0.0
N
0.0
Figure 6: The label distribution of samples under dif- ferent predictions of hypothesis-only model in debiased MNLI

---

masking rules
feature
p Po
i < mask the rest mask the rest mask words of P mask words of E
unigrams
mask the single word mask common words P and H mask words of II mask words of C
lexical overlap
hypothesis-only prediction
claim-only prediction
Table List of masking rules. and denote the premise and hypothesis. C and E denote the claim and evidence. All the masking rules derive from random masking strategy: mask the words of the samples to fixed percentage.
B Hyper-Parameters
B.3 FEVER Model Training
We earry out all our experiments in Linux vironment with single RTX 3090 (24G). B.2, B.3 and B.4 list the details hyperparameters for fine-tuning a BERT-base model. For roberta-base. roberta-large, and Albert, we fix other hyperparam- eters and just change the batch size (32 for roberta- base, 16 for roberta-large. and for Albert) due to the limitation of GPU memory. We roughly spend 30 hours to debias MNLI and 20 hours for FEVER and QQP.
Hyperparameter
Value
Learning rate
le-5
Batch size
8
Loss
Cross-Entropy
Epoch
5
Optimizer
Adam(default)
Learning rate scheduler
Linear
Warm up steps
1500
Max sequence length
128
Table 12:
Hyperparameter for training models
FEVER and our debiased FEVER
B.1 Perturbations
B.4
QQP Model Training
Hyperparameter
Value
Masking ratio for unigram
0.25
Hyperparameter
Value
Masking ratio H-only prediction
0.4
Masking ratio for lexical overlap
0.4
Learning rate
le-5
Threshold 7
20
Batch size
16
Pre-trained MLM
Xlrge
Loss
Cross-Entropy
Epoch
5
Table 10: Hyperparameter for perturbations
Optimizer
Adam(default)
Learning rate scheduler
Linear
Warm up steps
2000
B.2 ILI Model Training
Max sequence length
128
Table 13: Hyperparameter for training models on QQP and our debiased QQP
Hyperparameter
Value
Learning rate
2e-5
Batch size
32
Loss
Cross-Entropy
C Reducing Z-values
Epoch
5
Given feature x and label I, the object z-value to optimize is
Optimizer
Adam (default)
Learning rate scheduler
Linear
Warm up steps
2000
1 Po
2 (1, Vpo(1 Po)/n
Max sequence length
128
Table II: Hyperparameter for training NIII models on MNLI and our debiased MNLI
We donate the number samples under each label as 7h, B2.... T.k. n.
X 11 p= m

---

Reducing z-value equals to reduce
p po
21(,,)) = ( Vpo(1 Pn)/n
)
x (f po)
When we conduct the perturbations we can only see a sample with its label I, so we just consider partial derivative along ny:
(1,x)² On
2n n
nh1 2
2po + po
n
(2 + po + (po p)
Hence the operation ny reduce v-vulue
tually work by minimizing the gap between P and Po- For p < Po we increase á by ny and for p < Po we decrease p by ni 1.
D Details of Combining PoE with PDD
D.1 Bias-Only Model for Hypothesis-Only Bias
We consider the BERT-base model that can only see the hypothesis during training to model the artifacts in the hypothesis.
D.2 Bias-Only Model for Syntactic Bias
Instead of using the manually designed features proposed by (Karimi Mahabadi et al., 2020), we train BERT-base model that only the ordered words capture the syntactic bias (Tang al., 2023).
E Perturbation Strategies
In Table 14, we illustrate our masking rules for MNLI with concrete examples.

---

unigrams
Original: P: It's kind of strange way things go here you have accident injured police show up.
The police don't show if nobody hurt.
I. entailment
Riused feature: a nohody p(1[2) <
Masking rule: Preserve nobody, randomly mask other words.
Masked: It's kind <mask> here the way we go uh here if you have an accident and no one's <mask> the police won't even <mask> up.
The police don't show up <mask> nobody <mask> hurt.
entaiimmnt
Perturbed: It's kind of weird
way go uh here you have accident and one's hurt the police won't even get up.
H: The police don't show up nobody gets hurt.
I. entailment
Original: P: One of our number will carry out your instractions minutely.
H: A member of our team will execute your orders with great precision.
E entailment
Biased feature: J carry B(|[z) > 3
Masking rulc: Mask carry, randomly mask other words.
Masked: One
number will
your instructions minutely your <mask> with <masks <masks.
A member
team will
I: entailment
Perturbed: P. One of number will execute all your instructions minutely correct.
H: A member of our tearn will execute your instructions with immense efficiency.
E entailment
hypothesis-only prediction
Original: P: Muller and most his staff be expecteded not to cause any the usual mid-night disturbances.
H. Maller will most Likely cause more trouble.
I contradiction
Blased feature; prodiction of a hypothesis only model is entailment [(([) <
Masking rule: Preserve H, randomly mask P.
Masked: P: Muller and most of <mask> staff can he expecteded <masks tn cause <mask> more <mask> the usual pay-night disturbances. Muller will most likely cause more trouble.
1: contradiction
Pertubed: Moller
of staff be expecteded her much H: Muller will most likely
usual pay-night disturbances.
trouble.
I: contradiction
Original: P: My walkman hrike so m upset now just have tn turn the steneo up real loud.
H: I'm upset because my walkman broken and now I have to turn the stereo really lond.
I: entailment
Blased feature: x= prediction of hypothesis-only model is entailment ((la)) >
Masking rule; Preserve P. randomly mask
Masked: My walkman broke so upset now just turn my stereo up loud.
I'm <mask> <mask> my walkman broken and now have turn my up really <masko.
I entailment
Pertured:: P: My walkman broke so i' upset now just have to stereo up real loud.
H: I'm that my walkman broken and now I have to turn my stereo really high.
I entailment
Texical overlup
Original: P: She had to know Belly Curric very well.
H: She had come quite distike Betty Curric.
I: contradiction
Biased feature; word overlap between H and P 0.8 p(l))) $
Masking rule; Ramdomly musk unique worls and H. Pertureed: Pt She had come to emask> Betty Currie very well. She had come to cmask> dishikeBetty Currie.
I contradiction
Perturbed:
Currie very well. H. She had come really dislike Betty Currie.
She had
I: contradiction
Original: P: They were pushing the pace all right.
H: They were pushing the pace.
I: entailment
Biased feature: x word overlap between H and P All)) >
Masking rule; Randomly mask words shared
Masked: They <masis the pace all right.
H: They <mask> pushing the <masko.
I entailment
Perturbed: P: They were cleaning the pace all right.
H. They are pushing the car.
I: entailment
Table 14; demonstration of how we conduct our perturbations under different situations for NLI.


Reviews:
[{"review_id": "JacO3bXxJz", "reviewer": "Reviewer_TmsF", "summary": "", "questions": "- Is there any sense that the method could be generalized to enable automatic discovery of the spurious correlations and the features that are responsible for the bias and OOD performance drop?\n- PDD is applied only so long as the z-values of the biasing features are not uniform. On the other hand, one might imagine running the algorithm for longer to generate even more perturbed samples (making sure that the z-values remain uniform), thereby using it as a data augmentation procedure. Would that help improving performance even further or does the performance improvement plateaus very quickly as soon as the z-values are uniform?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "- Comments & Q1_A: \"Limitation and reliance on prior knowledge\" \\\nAccording to the framework of PDD, the limitation and the heavy reliance on prior knowledge are attributed to the manually-designed task-independent features. An automatic way is using a model that is able to capture various spurious correlations. For example, we can use the prediction of a shallow model trained on a small subset of training sets as the feature (Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna 686 Gurevych. 2020b. Towards debiasing NLU models 687 from unknown biases. In EMNLP 2020.) and then debias the dataset.  We will explore its feasibility in future work. \n- Q1_B:\"The potential to serve as a data augmentation procedure\" \\\nSince the threshold of z-values is adjustable, we can set threshold = 0 to make sure the perturbation procedure will never stop in order to generate more samples while keeping the z-values uniform. We conduct the experiment on the debiased MNLI (590K) and produce a larger dataset containing 740K samples but find no obvious performance improvement. And we will try to investigate the influence of data size on OOD performance in our analysis part.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The debiasing method proposed in the paper is duly benchmarked against multiple state-of-the-art debiasing strategies\n- The proposed debiasing method is a data-centric approach that can be combined with any available model-centric debiasing method to take advantage of the combined capabilities\n- The proposed debiasing method matches or beats the performance of competitor methods on both ID and OOD datasets", "reasons_to_reject": "- The work is limited to NLI and Fact verification tasks, the biases that this specific type of task displays, and the features that capture them\n- The method is highly reliant on domain knowledge to identify the features onto which the spurious correlations latch to drive the bias", "questions_to_authors": "- Is there any sense that the method could be generalized to enable automatic discovery of the spurious correlations and the features that are responsible for the bias and OOD performance drop?\n- PDD is applied only so long as the z-values of the biasing features are not uniform. On the other hand, one might imagine running the algorithm for longer to generate even more perturbed samples (making sure that the z-values remain uniform), thereby using it as a data augmentation procedure. Would that help improving performance even further or does the performance improvement plateaus very quickly as soon as the z-values are uniform?", "paper_topic_and_main_contributions": "The paper deals with the problem of bias in NLU datasets, specifically in the form of spurious correlations between the features and labels that models tend to latch on for their prediction, but that fail to generalize to out-of-distribution (OOD) situations, therefore causing a performance drop. To address this issue the paper proposes a method to debias training datasets by augmenting them with new generated samples crafted so as to remove the spurious correlations that drive the bias are removed. The new samples are generated via a perturbation of the original training samples obtained through a pretrained mask language model and task-specific prompt templates in order to enforce a conditioning on the label when generating the new sample.\nThe method is evaluated on two NLU tasks, NLI and Fact Verification, and is shown to outperform state-of-the-art debiasing methods on both in-domain and out-of-domain datasets.\n\n**Post-rebuttal period changes:**\n* Increased \"Excitement\" to \"strong\"\n* Increased \"Confidence\" to \"pretty sure\"", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "gJvTrIXxh8", "reviewer": "Reviewer_gCCW", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your acknowledging our framework as effective, easy-to-reproduce, and cost-effective. We will address your concerns below, and we humbly hope that you will consider increasing the evaluation score.\n- About improvement \\\nThe results show that PDD has achieved comparable performances with SOTA methods . Additionally, PDD is model-agnostic and can further benefit from other model-centric methods.\n- About novelty \\\nThank you for raising the concern regarding the novelty of our study. We appreciate your attention to previously published studies in the field. We would like to address your comment and explain why our study still contributes to the existing knowledge and advances the field.\nWhile it is true that the studies by Wu et al. have adopted the framework of z-statistics, they still follow the regular way in which they finetune a generator and then filter generated samples. The finetuning-then-filtering pattern is not a perfect fit for the z-statistics framework. The z-statistics only works in the filtration process and is agnostic to the generation process.\nHowever, in PDD, we attempt to further explore the potential of the z-statistic and design the whole algorithm around the z-statistic framework. We combine the advantages of PLMs and z-statistics and successfully achieve the goal of debiasing datasets while saving the trouble of training generators. Additionally, in spite of a similar definition of debiased datasets, the generation strategy and the details are quite different between PDD and Z-aug.\nTherefore, we humbly believe that PDD is a not trivial extension but a meaningful attempt to explore the potential of z-statistics in debiasing NLU datasets.\n- Experiments on QQP/PAWS\\\nFollowing your suggestion, we conduct the experiments on QQP/PAWs. We select \"word overlap < 0.8\" and \"unigrams\" as task-independent features and find PDD also helps to improve model's performance on PAWS (42.3%) and achieve a gain of 6.5% against the BERT baseline (35.8%). The performance is comparable with Regularized-conf (43.0%) but is inferior to Reweighting(48%) and PoE (55.7%). Combined with PoE, the accuracy reaches 57.9%.\n- Complexity of PDD\\\nNotice that those sample-reweighting methods are model-level and orthogonal to our data-level framework. So it is not appropriate to directly compare them. Compared with existing data-level methods such as Z-aug, AFLITE, TAILOR and so on, PDD exhibits no more complexity.\n- Incomprehensive ablations and analysis\\\nWe thank all the reviewers for providing constructive suggestions and helping us further enrich the analysis. We will enrich the ablation experiments to cover more components of PDD in the revised version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper proposes a framework for data-centric debiasing methods with low cost, and this method obtain gains in 2 tasks.", "reasons_to_reject": "1. The performance improvement is weak, and is not novel.\n2. The paper should conduct experiments on QQP and the corresponding PAWS dataset.\n3. The ablations and analysis should be more comprehensive.", "questions_to_authors": "", "paper_topic_and_main_contributions": "Existing data-centric approaches for debiasing models are still faced with the limitation of high cost, this paper proposes a framework that conducts training-free perturbations on samples containing biased features to debias NLU datasets. This framework is cost-effective, and gets improvement in several OOD test sets.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "zOctwA3OsQ", "reviewer": "Reviewer_syBo", "summary": "", "questions": "- Given that you mention throughout the paper that the method is cost-effective, have you considered demonstrating that quantitatively? \n- Have you considered training the model on e.g. MNLI and then evaluating transfer performance on multiple out-of-domain test sets? (see: Rabeeh Karimi Mahabadi, Yonatan Belinkov, and James Henderson. 2020. End-to-end bias mitigation by modelling biases in corpora. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.)", "limitations": "", "rating": 3, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We humbly believe that we have addressed your concerns below, and hope that you will consider increasing the evaluation score.\n- comments1: About novelty \\\nThank you for raising the concern regarding the novelty of our study. We appreciate your attention to previously published studies in the field. We would like to address your comment and explain why our study still contributes to the existing knowledge and advances the field.\\\nWhile it is true that the studies by Wu et al. have adopted the framework of z-statistics, they still follow the regular way in which they finetune a generator and then filter generated samples. The finetuning-then-filtering pattern is not a perfect fit for the z-statistics framework. The z-statistics only works in the filtration process and is agnostic to the generation process. \\\nHowever, in PDD, we attempt to further explore the potential of the z-statistic and design the whole algorithm around the z-statistic framework. We combine the advantages of PLMs and z-statistics and successfully achieve the goal of debiasing datasets while saving the trouble of training generators. Additionally, in spite of a similar definition of debiased datasets, the generation strategy and the details are quite different between PDD and Z-aug. \\\nTherefore, we humbly believe that PDD is a not trivial extension but a meaningful attempt to explore the potential of z-statistics in debiasing NLU datasets.\n- comments2: Experiments on QQP/PAWS\\\nFollowing your suggestion, we conduct the experiments on QQP/PAWs. We select \"word overlap < 0.8\" and \"unigrams\" as task-independent features. Experimental results show that PDD also helps to improve model's performance on PAWS (42.3%) and achieve a gain of 6.5% against the BERT baseline (35.8%). The performance is comparable with Regularized-conf (43.0%) but is inferior to Reweighting(48%) and PoE (55.7%). Combined with PoE, the accuracy reaches 57.9%.\n- comments3: Comparison with methods that do not assume prior knowledge about spurious correlations.\\\nWe thank the reviewer for pointing out this issue. We will compare PDD with several SOTA automatic debiasing methods in the revised version.\n- comments4: Incomprehensive ablations and analysis\\\nWe thank the reviewer for providing constructive suggestions and helping us further enrich the analysis. We will enrich the ablation experiments to cover more components of PDD in the revised version.\n- comments5: NLU to NLI \\\nThanks for the suggestion. We check the effectiveness of our framework in both NLI and Fact verification. And we conduct additional experiments on QQP/PAWS, so we prefer to remain \"NLU\".\n- Q3_A: Demonstrating cost quantitatively \\\nCompared to Z-aug (Wu et al., 2022) which reports 24 hours spent on training the generator and 35 hours spent on generating samples with a single A100, we roughly spend 30 hours to debias each dataset with a single RTX 3090. It takes about the same amount of time to generate the data and we save time training the generator.\n- Q3_B:  Transfer performance\\\nFollowing your suggestion, we evaluate the transfer performance of PDD on several datasets including MPE, MRPC, QNLI, WNLI, SCITAIL, SICK, and RTE. We find that the BERT-base trained on debiased MNLI totally get a 0.87% gain on average and performs better than the baseline on 6 test sets (MPE, MRPC, QNLI, WNLI, SCITAIL, and RTE). We will add this part to our analysis section.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Paper is well written and easy to follow.", "reasons_to_reject": "- Novelty of the method is limited. Overall, this paper feels like a trivial extension of prior work (Wu et al., 2022).\n- No experiments on QQP/PAWS and performance improvements are marginal at best, and the proposed method is considerably more complex compared to prior methods that rely on simple example re-weighting (e.g. Mahabadi et al., 2020).\n- No performance comparisons with methods that do not assume prior knowledge about spurious correlations.\n- Analysis/Ablations are far from comprehensive.\n- NLU is a very broad term, given the datasets you are using for examples it would be more suitable to change the title and text from NLU to NLI (or alternatively, consider conducting additional experiments on tasks such as QA).", "questions_to_authors": "- Given that you mention throughout the paper that the method is cost-effective, have you considered demonstrating that quantitatively? \n- Have you considered training the model on e.g. MNLI and then evaluating transfer performance on multiple out-of-domain test sets? (see: Rabeeh Karimi Mahabadi, Yonatan Belinkov, and James Henderson. 2020. End-to-end bias mitigation by modelling biases in corpora. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.)", "paper_topic_and_main_contributions": "This paper proposes a method for training models that are robust to spurious correlations. The method works by conducting perturbations on biased examples. The authors evaluate their proposed method on MNLI and FEVER.\n\n**Post rebuttal changes**\n1. Increased Soundness score (+1)\n2. Increased Excitement score (+1)", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper introduces a novel data-augmentation method to reduce spurious correlations when training on NLU datasets. Proposed method uses another pre-trained Masked-LM to generate perturbations input features and therefore reduces the cost compared to previous work (Z-aug). Reviewers agree on the soundness of the results and find them interesting.
Decision: Accept-Findings