Generating Data to Mitigate Spurious Correlations in Natural Language
Inference Datasets
- URL: http://arxiv.org/abs/2203.12942v1
- Date: Thu, 24 Mar 2022 09:08:05 GMT
- Title: Generating Data to Mitigate Spurious Correlations in Natural Language
Inference Datasets
- Authors: Yuxiang Wu, Matt Gardner, Pontus Stenetorp and Pradeep Dasigi
- Abstract summary: Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they are trained on.
We propose to tackle this problem by generating a debiased version of a dataset, which can then be used to train a debiased, off-the-shelf model.
Our approach consists of 1) a method for training data generators to generate high-quality, label-consistent data samples; and 2) a filtering mechanism for removing data points that contribute to spurious correlations.
- Score: 27.562256973255728
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Natural language processing models often exploit spurious correlations
between task-independent features and labels in datasets to perform well only
within the distributions they are trained on, while not generalising to
different task distributions. We propose to tackle this problem by generating a
debiased version of a dataset, which can then be used to train a debiased,
off-the-shelf model, by simply replacing its training data. Our approach
consists of 1) a method for training data generators to generate high-quality,
label-consistent data samples; and 2) a filtering mechanism for removing data
points that contribute to spurious correlations, measured in terms of
z-statistics. We generate debiased versions of the SNLI and MNLI datasets, and
we evaluate on a large suite of debiased, out-of-distribution, and adversarial
test sets. Results show that models trained on our debiased datasets generalise
better than those trained on the original datasets in all settings. On the
majority of the datasets, our method outperforms or performs comparably to
previous state-of-the-art debiasing strategies, and when combined with an
orthogonal technique, product-of-experts, it improves further and outperforms
previous best results of SNLI-hard and MNLI-hard.
Related papers
- Group Distributionally Robust Dataset Distillation with Risk
Minimization [18.07189444450016]
We introduce an algorithm that combines clustering with the minimization of a risk measure on the loss to conduct DD.
We demonstrate its effective generalization and robustness across subgroups through numerical experiments.
arXiv Detail & Related papers (2024-02-07T09:03:04Z) - Feature-Level Debiased Natural Language Understanding [86.8751772146264]
Existing natural language understanding (NLU) models often rely on dataset biases to achieve high performance on specific datasets.
We propose debiasing contrastive learning (DCT) to mitigate biased latent features and neglect the dynamic nature of bias.
DCT outperforms state-of-the-art baselines on out-of-distribution datasets while maintaining in-distribution performance.
arXiv Detail & Related papers (2022-12-11T06:16:14Z) - Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity
for Robust Left-Right Eye-Tracking Classifiers [0.0]
We train machine learning models utilizing a mixed dataset composed of both fine- and coarse-grain data.
For our purposes, finer-grain data refers to data collected using more complex methods whereas coarser-grain data refers to data collected using more simple methods.
arXiv Detail & Related papers (2022-08-24T23:18:08Z) - General Greedy De-bias Learning [163.65789778416172]
We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space.
GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
arXiv Detail & Related papers (2021-12-20T14:47:32Z) - Automatically Identifying Semantic Bias in Crowdsourced Natural Language
Inference Datasets [78.6856732729301]
We introduce a model-driven, unsupervised technique to find "bias clusters" in a learned embedding space of hypotheses in NLI datasets.
interventions and additional rounds of labeling can be performed to ameliorate the semantic bias of the hypothesis distribution of a dataset.
arXiv Detail & Related papers (2021-12-16T22:49:01Z) - CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact
Verification Models [14.75693099720436]
We propose CrossAug, a contrastive data augmentation method for debiasing fact verification models.
We employ a two-stage augmentation pipeline to generate new claims and evidences from existing samples.
The generated samples are then paired cross-wise with the original pair, forming contrastive samples that facilitate the model to rely less on spurious patterns.
arXiv Detail & Related papers (2021-09-30T13:19:19Z) - Examining and Combating Spurious Features under Distribution Shift [94.31956965507085]
We define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics.
We prove that even when there is only bias of the input distribution, models can still pick up spurious features from their training data.
Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations.
arXiv Detail & Related papers (2021-06-14T05:39:09Z) - Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles [66.15398165275926]
We propose a method that can automatically detect and ignore dataset-specific patterns, which we call dataset biases.
Our method trains a lower capacity model in an ensemble with a higher capacity model.
We show improvement in all settings, including a 10 point gain on the visual question answering dataset.
arXiv Detail & Related papers (2020-11-07T22:20:03Z) - Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
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.
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.
arXiv Detail & Related papers (2020-05-10T17:56:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.