Margaret Mitchell


2021

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Documenting Large Webtext Corpora : A Case Study on the Colossal Clean Crawled Corpus
Jesse Dodge | Maarten Sap | Ana Marasović | William Agnew | Gabriel Ilharco | Dirk Groeneveld | Margaret Mitchell | Matt Gardner
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4 ; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites. Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets. To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals. Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.

2019

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Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Vinodkumar Prabhakaran | Ben Hutchinson | Margaret Mitchell
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language. However, most language data reflect the public discourse at the time the data was produced, and hence NLP models are susceptible to learning incidental associations around named referents at a particular point in time, in addition to general linguistic meaning. An NLP system designed to model notions such as sentiment and toxicity should ideally produce scores that are independent of the identity of such entities mentioned in text and their social associations. For example, in a general purpose sentiment analysis system, a phrase such as I hate Katy Perry should be interpreted as having the same sentiment as I hate Taylor Swift. Based on this idea, we propose a generic evaluation framework, Perturbation Sensitivity Analysis, which detects unintended model biases related to named entities, and requires no new annotations or corpora. We demonstrate the utility of this analysis by employing it on two different NLP models a sentiment model and a toxicity model applied on online comments in English language from four different genres.

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Proceedings of the Second Workshop on Storytelling
Francis Ferraro | Ting-Hao ‘Kenneth’ Huang | Stephanie M. Lukin | Margaret Mitchell
Proceedings of the Second Workshop on Storytelling

2018

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Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing
Mark Alfano | Dirk Hovy | Margaret Mitchell | Michael Strube
Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing

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Proceedings of the First Workshop on Storytelling
Margaret Mitchell | Ting-Hao ‘Kenneth’ Huang | Francis Ferraro | Ishan Misra
Proceedings of the First Workshop on Storytelling

2017

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Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
Dirk Hovy | Shannon Spruit | Margaret Mitchell | Emily M. Bender | Michael Strube | Hanna Wallach
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing

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Multitask Learning for Mental Health Conditions with Limited Social Media Data
Adrian Benton | Margaret Mitchell | Dirk Hovy
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Language contains information about the author’s demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.