Yada Pruksachatkun


2022

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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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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.

2021

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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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Proceedings of the First Workshop on Trustworthy Natural Language Processing
Yada Pruksachatkun | Anil Ramakrishna | Kai-Wei Chang | Satyapriya Krishna | Jwala Dhamala | Tanaya Guha | Xiang Ren
Proceedings of the First Workshop on Trustworthy Natural Language Processing

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CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes
James Mullenbach | Yada Pruksachatkun | Sean Adler | Jennifer Seale | Jordan Swartz | Greg McKelvey | Hui Dai | Yi Yang | David Sontag
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task.

2020

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Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?
Yada Pruksachatkun | Jason Phang | Haokun Liu | Phu Mon Htut | Xiaoyi Zhang | Richard Yuanzhe Pang | Clara Vania | Katharina Kann | Samuel R. Bowman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a target task. However, it is still poorly understood when and why intermediate-task training is beneficial for a given target task. To investigate this, we perform a large-scale study on the pretrained RoBERTa model with 110 intermediate-target task combinations. We further evaluate all trained models with 25 probing tasks meant to reveal the specific skills that drive transfer. We observe that intermediate tasks requiring high-level inference and reasoning abilities tend to work best. We also observe that target task performance is strongly correlated with higher-level abilities such as coreference resolution. However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks. We also observe evidence that the forgetting of knowledge learned during pretraining may limit our analysis, highlighting the need for further work on transfer learning methods in these settings.

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jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
Yada Pruksachatkun | Phil Yeres | Haokun Liu | Jason Phang | Phu Mon Htut | Alex Wang | Ian Tenney | Samuel R. Bowman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration driven experimentation with state-of-the-art models and a broad set of tasks for probing, transfer learning, and multitask training experiments. jiant implements over 50 NLU tasks, including all GLUE and SuperGLUE benchmark tasks. We demonstrate that jiant reproduces published performance on a variety of tasks and models, e.g., RoBERTa and BERT.

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English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
Jason Phang | Iacer Calixto | Phu Mon Htut | Yada Pruksachatkun | Haokun Liu | Clara Vania | Katharina Kann | Samuel R. Bowman
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Intermediate-task training—fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task—often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tasks and moderate improvements on question-answering target tasks. MNLI, SQuAD and HellaSwag achieve the best overall results as intermediate tasks, while multi-task intermediate offers small additional improvements. Using our best intermediate-task models for each target task, we obtain a 5.4 point improvement over XLM-R Large on the XTREME benchmark, setting the state of the art as of June 2020. We also investigate continuing multilingual MLM during intermediate-task training and using machine-translated intermediate-task data, but neither consistently outperforms simply performing English intermediate-task training.