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Sociolinguistically Driven Approaches for Just Natural Language Processing University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses April 2021 Sociolinguistically Driven Approaches for Just Natural Language Processing Su Lin Blodgett University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/dissertations_2 Part of the Artificial Intelligence and Robotics Commons Recommended Citation Blodgett, Su Lin, "Sociolinguistically Driven Approaches for Just Natural Language Processing" (2021). Doctoral Dissertations. 2092. https://doi.org/10.7275/20410631 https://scholarworks.umass.edu/dissertations_2/2092 This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. SOCIOLINGUISTICALLY DRIVEN APPROACHES FOR JUST NATURAL LANGUAGE PROCESSING A Dissertation Presented by SU LIN BLODGETT Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY February 2021 College of Information and Computer Sciences © Copyright by Su Lin Blodgett 2021 All Rights Reserved SOCIOLINGUISTICALLY DRIVEN APPROACHES FOR JUST NATURAL LANGUAGE PROCESSING A Dissertation Presented by SU LIN BLODGETT Approved as to style and content by: Brendan O’Connor, Chair Mohit Iyyer, Member Hanna Wallach, Member Lisa Green, Member James Allan, Chair of the Faculty College of Information and Computer Sciences ACKNOWLEDGMENTS First and foremost, I would like to thank my advisor, Brendan O’Connor, who introduced me to natural language processing and computational sociolinguistics, taught me more than I can say about research, NLP, and writing, and has been endlessly supportive of my many and growing research interests; I consider myself amazingly lucky to have stumbled into his lab. I am also very grateful to my thesis committee members, Mohit Iyyer, Hanna Wallach, and Lisa Green, for their thoughtful feedback and support; in particular, I would like to thank Hanna and Lisa for their invaluable feedback during our collaborations. I would also like to thank Solon Barocas and Hal Daumé III, who along with Hanna were (and continue to be) incredibly generous and intellectually inspiring mentors and collaborators. I am also grateful to Johnny Wei for his thoughtful work during our collaboration. I have been lucky to pursue this PhD alongside my wonderful labmates Abe Handler and Katie Keith, who over the years have been the source of amazing discussions, inspiration, and support. I’ve also been fortunate to be part of a fantastic community at UMass, in- cluding fellow graduate students Lucas Chaufournier, Kaleigh Clary, Janet Guo, Myungha Jang, Neha Kennard, Kalpesh Krishna, Tiffany Liu, Dirk Ruiken, Rian Shambaugh, Emma Tosch, and Kevin Winner. I’m also grateful to friends outside of UMass who were an impor- tant source of research inspiration and support, including Michael Madaio, Ian Stewart, and Zeerak Waseem. Additionally, I’d like to thank the administrative staff at UMass, partic- ularly Leeanne Leclerc, Gwyn Mitchell, Eileen Hamel, and Malaika Ross. I’m also grateful to Sohie Lee, who encouraged me to pursue a PhD. Finally, I am forever indebted to my parents, sister, and Samer for their boundless support, encouragement, and love. iv ABSTRACT SOCIOLINGUISTICALLY DRIVEN APPROACHES FOR JUST NATURAL LANGUAGE PROCESSING FEBRUARY 2021 SU LIN BLODGETT B.A., WELLESLEY COLLEGE M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Brendan O’Connor Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying dispari- ties in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey v of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxon- omy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work. vi TABLE OF CONTENTS Page ACKNOWLEDGMENTS ............................................. iv ABSTRACT .......................................................... v LIST OF TABLES ................................................... xii LIST OF FIGURES.................................................. xv CHAPTER 1. INTRODUCTION ................................................. 1 2. VARIATION ON TWITTER: DEVELOPING A CORPUS OF AAL ............................................................ 4 2.1 Introduction . .4 2.1.1 What is African American Language? . .5 2.2 Identifying AAL from demographics . .6 2.2.1 AAL on Twitter . .6 2.2.2 Twitter and Census data . .7 2.2.3 Direct word-demographic analysis . .8 2.2.4 Mixed-membership demographic language model . .9 2.3 Linguistic validation . 12 2.3.1 Lexical-level validation . 13 2.3.2 Internet-specific orthography . 13 2.3.3 Phonological validation . 14 2.3.4 Syntactic validation . 14 2.4 A dataset for AAL morphosyntactic analysis . 16 2.4.1 Related work . 17 vii 2.4.2 Dataset goals . 19 2.4.3 Data collection and analysis . 22 2.5 Conclusion . 26 3. FAIRNESS IN NLP TOOLS: LANGUAGE IDENTIFICATION ............................................ 27 3.1 Introduction . 27 3.2 Related work . 28 3.3 Twitter and langid.py ............................................ 28 3.3.1 Adapting language identification for AAL. 29 3.4 Commercial systems . 31 3.4.1 Experiments . 31 3.5 Extended evaluation: World languages . 34 3.5.1 Dataset . 34 3.5.2 Experiments . 36 3.5.3 Results and discussion . 37 4. FAIRNESS IN NLP TOOLS: DEPENDENCY PARSING ......... 42 4.1 Introduction . 42 4.2 Related Work . 42 4.2.1 Parsing for Twitter . 42 4.2.2 Parsing for non-standard varieties . 43 4.3 Preliminary analysis: Stanford dependencies. 44 4.4 Extended analysis: Universal dependencies . 45 4.4.1 Dataset . 46 4.4.2 AAL annotation . 46 4.4.3 Non-AAL Twitter annotation . 49 4.4.4 Experiments . 53 4.4.5 Results and analysis . 57 4.5 Discussion and conclusion. 63 5. MEASURING BIAS: A SURVEY OF BIAS IN NLP .............. 64 5.1 Introduction . 64 5.2 A critical analysis of bias in NLP . 65 viii 5.2.1 Method for gathering papers . 66 5.2.2 Findings . 68 5.3 Conclusion . 74 6. LANGUAGE AND JUSTICE ..................................... 75 6.1 Introduction . 75 6.2 Social justice . 75 6.2.1 Social justice and technology. 77 6.3 Overview of language and justice . 80 6.3.1 Language about ........................................... 81 6.3.2 Language by .............................................. 84 6.3.3 Case study: African American Language . 87 6.3.4 Takeaways . 91 7. MEASURING BIAS: A TAXONOMY OF HARMS ............... 93 7.1 Introduction . 93 7.2 Towards a taxonomy of representational harms . 93 7.2.1 Undesirable correlations . 94 7.2.2 A taxonomy of representational harms . 96 7.2.3 Other dynamics and effects . 107 7.2.4 Other taxonomies of harms in NLP . 110 7.3 Discussion and recommendations . 114 7.3.1 Language and unjust social arrangements . 115 7.3.2 Conceptualizations of bias . 116 7.3.3 Language use in practice . 118 7.3.4 Case study . 119 8. MEASURING BIAS: EVALUATING MEASUREMENTS OF BIAS ......................................................... 123 8.1 Introduction . 123 8.2 Measurement modeling . ..
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