Hanna Wallach

Also published as: Hanna M. Wallach


2021

pdf bib
Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing
Su Lin Blodgett | Michael Madaio | Brendan O'Connor | Hanna Wallach | Qian Yang
Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing

pdf bib
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
Adina Williams | Ryan Cotterell | Lawrence Wolf-Sonkin | Damián Blasi | Hanna Wallach
Transactions of the Association for Computational Linguistics, Volume 9

Abstract We use large-scale corpora in six different gendered languages, along with tools from NLP and information theory, to test whether there is a relationship between the grammatical genders of inanimate nouns and the adjectives used to describe those nouns. For all six languages, we find that there is a statistically significant relationship. We also find that there are statistically significant relationships between the grammatical genders of inanimate nouns and the verbs that take those nouns as direct objects, as indirect objects, and as subjects. We defer deeper investigation of these relationships for future work.

pdf bib
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets
Su Lin Blodgett | Gilsinia Lopez | Alexandra Olteanu | Robert Sim | Hanna Wallach
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)

Auditing NLP systems for computational harms like surfacing stereotypes is an elusive goal. Several recent efforts have focused on benchmark datasets consisting of pairs of contrastive sentences, which are often accompanied by metrics that aggregate an NLP system’s behavior on these pairs into measurements of harms. We examine four such benchmarks constructed for two NLP tasks: language modeling and coreference resolution. We apply a measurement modeling lens—originating from the social sciences—to inventory a range of pitfalls that threaten these benchmarks’ validity as measurement models for stereotyping. We find that these benchmarks frequently lack clear articulations of what is being measured, and we highlight a range of ambiguities and unstated assumptions that affect how these benchmarks conceptualize and operationalize stereotyping.

2020

pdf bib
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Su Lin Blodgett | Solon Barocas | Hal Daumé III | Hanna Wallach
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We survey 146 papers analyzing “bias” in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing “bias” is an inherently normative process. We further find that these papers’ proposed quantitative techniques for measuring or mitigating “bias” are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing “bias” in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of “bias”---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements—and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.

2019

pdf bib
Quantifying the Semantic Core of Gender Systems
Adina Williams | Damian Blasi | Lawrence Wolf-Sonkin | Hanna Wallach | Ryan Cotterell
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Many of the world’s languages employ grammatical gender on the lexeme. For instance, in Spanish, house “casa” is feminine, whereas the word for paper “papel” is masculine. To a speaker of a genderless language, this categorization seems to exist with neither rhyme nor reason. But, is the association of nouns to gender classes truly arbitrary? In this work, we present the first large-scale investigation of the arbitrariness of gender assignment that uses canonical correlation analysis as a method for correlating the gender of inanimate nouns with their lexical semantic meaning. We find that the gender systems of 18 languages exhibit a significant correlation with an externally grounded definition of lexical semantics.

pdf bib
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Ran Zmigrod | Sabrina J. Mielke | Hanna Wallach | Ryan Cotterell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Gender stereotypes are manifest in most of the world’s languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present a novel approach for converting between masculine-inflected and feminine-inflected sentences in such languages. For Spanish and Hebrew, our approach achieves F1 scores of 82% and 73% at the level of tags and accuracies of 90% and 87% at the level of forms. By evaluating our approach using four different languages, we show that, on average, it reduces gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.

pdf bib
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling
Alexander Miserlis Hoyle | Lawrence Wolf-Sonkin | Hanna Wallach | Isabelle Augenstein | Ryan Cotterell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men.

pdf bib
Combining Sentiment Lexica with a Multi-View Variational Autoencoder
Alexander Miserlis Hoyle | Lawrence Wolf-Sonkin | Hanna Wallach | Ryan Cotterell | Isabelle Augenstein
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.

pdf bib
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes
Alexey Romanov | Maria De-Arteaga | Hanna Wallach | Jennifer Chayes | Christian Borgs | Alexandra Chouldechova | Sahin Geyik | Krishnaram Kenthapadi | Anna Rumshisky | Adam Kalai
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is often desirable to simultaneously consider multiple protected attributes, as well as their intersections. In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual’s true occupation and a word embedding of their name. This method leverages the societal biases that are encoded in word embeddings, eliminating the need for access to protected attributes. Crucially, it only requires access to individuals’ names at training time and not at deployment time. We evaluate two variations of our proposed method using a large-scale dataset of online biographies. We find that both variations simultaneously reduce race and gender biases, with almost no reduction in the classifier’s overall true positive rate.

2017

pdf bib
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

2016

pdf bib
Bag of What? Simple Noun Phrase Extraction for Text Analysis
Abram Handler | Matthew Denny | Hanna Wallach | Brendan O’Connor
Proceedings of the First Workshop on NLP and Computational Social Science

pdf bib
Detecting and Characterizing Events
Allison Chaney | Hanna Wallach | Matthew Connelly | David Blei
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2011

pdf bib
Optimizing Semantic Coherence in Topic Models
David Mimno | Hanna Wallach | Edmund Talley | Miriam Leenders | Andrew McCallum
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2009

pdf bib
Polylingual Topic Models
David Mimno | Hanna M. Wallach | Jason Naradowsky | David A. Smith | Andrew McCallum
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing