Varun Kumar


2022

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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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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.

2021

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Industry Scale Semi-Supervised Learning for Natural Language Understanding
Luoxin Chen | Francisco Garcia | Varun Kumar | He Xie | Jianhua Lu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how does the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.

2020

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Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems
William M. Campbell | Alex Waibel | Dilek Hakkani-Tur | Timothy J. Hazen | Kevin Kilgour | Eunah Cho | Varun Kumar | Hadrien Glaude
Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems

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Data Augmentation using Pre-trained Transformer Models
Varun Kumar | Ashutosh Choudhary | Eunah Cho
Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems

Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information.

2019

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A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification
Varun Kumar | Hadrien Glaude | Cyprien de Lichy | Wlliam Campbell
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance. We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used to introduce a new intent. In this paper, we study six feature space data augmentation methods to improve classification performance in FSI setting in combination with both supervised and unsupervised representation learning methods such as BERT. Through realistic experiments on two public conversational datasets, SNIPS, and the Facebook Dialog corpus, we show that data augmentation in feature space provides an effective way to improve intent classification performance in few-shot setting beyond traditional transfer learning approaches. In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.

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Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
Varun Kumar | Alison Smith-Renner | Leah Findlater | Kevin Seppi | Jordan Boyd-Graber
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations’ results match users’ expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.

2016

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The GW/UMD CLPsych 2016 Shared Task System
Ayah Zirikly | Varun Kumar | Philip Resnik
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology