Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle

Cao, Yang; Daumé, Hal

doi:10.1162/coli_a_00413

Cited by 24 publications

(24 citation statements)

References 134 publications

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“…Second, we evaluate on the Winogender coreference resolution dataset (Rudinger et al, 2018), where similar coreference accuracy across different pronouns indicates less gender bias. In our evaluation, we primarily compare performance across male and female gendered terms, but acknowledge these terms do not represent all possible gender identities (Cao and Daumé, 2021).…”

Section: Gender and Occupation Biasmentioning

confidence: 99%

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae¹,

Borgeaud²,

Cai³

et al. 2021

Preprint

121

169

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Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms.

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Section: Gender and Occupation Biasmentioning

confidence: 99%

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae¹,

Borgeaud²,

Cai³

et al. 2021

Preprint

121

169

View full text Add to dashboard Cite

show abstract

“…Similarly, when working on language data, it might be more appropriate to analyze data partitioned by linguistic gender (as opposed to social gender). See Cao and Daumé (2021) for a useful discussion on linguistic vs. social gender and also for a great example to create more inclusive data for research.…”

mentioning

confidence: 99%

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Mohammad

2022

Computational Linguistics

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The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this paper, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, the sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. Along the way, key recommendations are made for responsible AER. The objective of the sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the sheet acts as a useful introductory document on emotion recognition (complementing survey articles).

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“…As Raymond (2016) notes, pronoun choices construct the individual's identity in conversations and the relationship between interlocutors. According to Cao and Daumé III (2021), pronouns are a way of expressing referential gender. Referring to an indi- vidual with sets of pronouns they do not identify with, e.g., resulting in misgendering, is considered harmful (Dev et al, 2021).…”

Section: A Note On Identity and Pronounsmentioning

confidence: 99%

“…3 This "social push" to respect diverse gender identities also affects aspects of NLP. Recent studies have pointed out the potential harms from the cur-rent lack of non-binary representation in NLP data sets, embeddings, and tasks (Cao and Daumé III, 2021;Dev et al, 2021), and the related issue of unfair stereotyping of queer individuals (Barikeri et al, 2021). However, the research landscape on modern pronoun usage is still surprisingly scarce, hindering progress for a fair and inclusive NLP.…”

Section: Introductionmentioning

confidence: 99%

Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

Lauscher¹,

Crowley²,

Hovy³

2022

Preprint

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Trigger warning: This paper contains some examples which might be offensive to some users.The worls of pronouns is changing. From a closed class of words with few members to a much more open set of terms to reflect identities. However, Natural Language Processing (NLP) is barely reflecting this linguistic shift, even though recent work outlined the harms of gender-exclusive language technology. Particularly problematic is the current modeling 3rd person pronouns, as it largely ignores various phenomena like neopronouns, i.e., pronoun sets that are novel and not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g., non-binary individuals. However, other identity-expression phenomena beyond gender are also ignored by current NLP technology. In this paper, we provide an overview of 3rd person pronoun issues for NLP. Based on our observations and ethical considerations, we define a series of desiderata for modeling pronouns in language technology. We evaluate existing and novel modeling approaches w.r.t. these desiderata qualitatively, and quantify the impact of a more discrimination-free approach on established benchmark data.1 Essential definition provided by the Mirriam Webster Online Dictionaly at https://www.merriam-webste r.com/dictionary/pronoun 3 https://dictionnaire.lerobert.com/di s-moi-robert/raconte-moi-robert/mot-jo ur/pourquoi-le-robert-a-t-il-integre-lemot-iel-dans-son-dictionnaire-en-ligne.h tml

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Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle

Cited by 24 publications

References 134 publications

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

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