Rahul Gupta


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Measuring Fairness of Text Classifiers via Prediction Sensitivity
Satyapriya Krishna | Rahul Gupta | Apurv Verma | Jwala Dhamala | Yada Pruksachatkun | Kai-Wei Chang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system. In this work, we propose a new formulation – accumulated prediction sensitivity, which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. It also correlates well with humans’ perception of fairness. We conduct experiments on two text classification datasets – Jigsaw Toxicity, and Bias in Bios, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.

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Canary Extraction in Natural Language Understanding Models
Rahil Parikh | Christophe Dupuy | Rahul Gupta
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Natural Language Understanding (NLU) models can be trained on sensitive information such as phone numbers, zip-codes etc. Recent literature has focused on Model Inversion Attacks (ModIvA) that can extract training data from model parameters. In this work, we present a version of such an attack by extracting canaries inserted in NLU training data. In the attack, an adversary with open-box access to the model reconstructs the canaries contained in the model’s training set. We evaluate our approach by performing text completion on canaries and demonstrate that by using the prefix (non-sensitive) tokens of the canary, we can generate the full canary. As an example, our attack is able to reconstruct a four digit code in the training dataset of the NLU model with a probability of 0.5 in its best configuration. As countermeasures, we identify several defense mechanisms that, when combined, effectively eliminate the risk of ModIvA in our experiments.

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On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations
Yang Cao | Yada Pruksachatkun | Kai-Wei Chang | Rahul Gupta | Varun Kumar | Jwala Dhamala | Aram Galstyan
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) extrinsic metrics for evaluating fairness in downstream applications and 2) intrinsic metrics for estimating fairness in upstream contextualized language representation models. In this paper, we conduct an extensive correlation study between intrinsic and extrinsic metrics across bias notions using 19 contextualized language models. We find that intrinsic and extrinsic metrics do not necessarily correlate in their original setting, even when correcting for metric misalignments, noise in evaluation datasets, and confounding factors such as experiment configuration for extrinsic metrics.

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Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal
Umang Gupta | Jwala Dhamala | Varun Kumar | Apurv Verma | Yada Pruksachatkun | Satyapriya Krishna | Rahul Gupta | Kai-Wei Chang | Greg Ver Steeg | Aram Galstyan
Findings of the Association for Computational Linguistics: ACL 2022

Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model’s biases onto the distilled model. To this end, we present a novel approach to mitigate gender disparity in text generation by learning a fair model during knowledge distillation. We propose two modifications to the base knowledge distillation based on counterfactual role reversal—modifying teacher probabilities and augmenting the training set. We evaluate gender polarity across professions in open-ended text generated from the resulting distilled and finetuned GPT–2 models and demonstrate a substantial reduction in gender disparity with only a minor compromise in utility. Finally, we observe that language models that reduce gender polarity in language generation do not improve embedding fairness or downstream classification fairness.


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ADePT: Auto-encoder based Differentially Private Text Transformation
Satyapriya Krishna | Rahul Gupta | Christophe Dupuy
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Privacy is an important concern when building statistical models on data containing personal information. Differential privacy offers a strong definition of privacy and can be used to solve several privacy concerns. Multiple solutions have been proposed for the differentially-private transformation of datasets containing sensitive information. However, such transformation algorithms offer poor utility in Natural Language Processing (NLP) tasks due to noise added in the process. This paper addresses this issue by providing a utility-preserving differentially private text transformation algorithm using auto-encoders. Our algorithm transforms text to offer robustness against attacks and produces transformations with high semantic quality that perform well on downstream NLP tasks. We prove our algorithm’s theoretical privacy guarantee and assess its privacy leakage under Membership Inference Attacks (MIA) on models trained with transformed data. Our results show that the proposed model performs better against MIA attacks while offering lower to no degradation in the utility of the underlying transformation process compared to existing baselines.

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Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Yada Pruksachatkun | Satyapriya Krishna | Jwala Dhamala | Rahul Gupta | Kai-Wei Chang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Towards Realistic Single-Task Continuous Learning Research for NER
Justin Payan | Yuval Merhav | He Xie | Satyapriya Krishna | Anil Ramakrishna | Mukund Sridhar | Rahul Gupta
Findings of the Association for Computational Linguistics: EMNLP 2021

There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications. Meanwhile, there is still a lack of academic NLP benchmarks that are applicable for realistic CL settings, which is a major challenge for the advancement of the field. In this paper we discuss some of the unrealistic data characteristics of public datasets, study the challenges of realistic single-task continuous learning as well as the effectiveness of data rehearsal as a way to mitigate accuracy loss. We construct a CL NER dataset from an existing publicly available dataset and release it along with the code to the research community.


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Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks
Ansel MacLaughlin | Jwala Dhamala | Anoop Kumar | Sriram Venkatapathy | Ragav Venkatesan | Rahul Gupta
Proceedings of the First Workshop on Insights from Negative Results in NLP

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification. In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and semantic textual similarity. We use ENAS to perform a micro-level search and learn a task-optimized RNN cell architecture as a drop-in replacement for an LSTM. We explore the effectiveness of ENAS through experiments on three datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and two sets of embeddings (Glove, BERT). In contrast to prior work applying ENAS to NLP tasks, our results are mixed – we find that ENAS architectures sometimes, but not always, outperform LSTMs and perform similarly to random architecture search.


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ReNoun: Fact Extraction for Nominal Attributes
Mohamed Yahya | Steven Whang | Rahul Gupta | Alon Halevy
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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An Evaluation Corpus For Temporal Summarization
Vikash Khandelwal | Rahul Gupta | James Allan
Proceedings of the First International Conference on Human Language Technology Research

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Monitoring the News: a TDT demonstration system
David Frey | Rahul Gupta | Vikas Khandelwal | Victor Lavrenko | Anton Leuski | James Allan
Proceedings of the First International Conference on Human Language Technology Research