Toward Gender-Inclusive Coreference Resolution

Cao, Yang; Daumé, Hal

doi:10.18653/v1/2020.acl-main.418

Cited by 61 publications

(53 citation statements)

References 158 publications

(50 reference statements)

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“…Among the biases Caliskan and colleagues found are associations of gender from names and careers (e.g., female names more associated with family than career words; more associated with the arts than with mathematics). Gender biases have also been found in coreference resolution [65,66], visual semantic role labelling [67], and machine translation [68,69].…”

Section: Gender Prediction and Gender Bias In Natural Language Processingmentioning

confidence: 99%

The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media

et al. 2021

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We examine gender bias in media by tallying the number of men and women quoted in news text, using the Gender Gap Tracker, a software system we developed specifically for this purpose. The Gender Gap Tracker downloads and analyzes the online daily publication of seven English-language Canadian news outlets and enhances the data with multiple layers of linguistic information. We describe the Natural Language Processing technology behind this system, the curation of off-the-shelf tools and resources that we used to build it, and the parts that we developed. We evaluate the system in each language processing task and report errors using real-world examples. Finally, by applying the Tracker to the data, we provide valuable insights about the proportion of people mentioned and quoted, by gender, news organization, and author gender. Data collected between October 1, 2018 and September 30, 2020 shows that, in general, men are quoted about three times as frequently as women. While this proportion varies across news outlets and time intervals, the general pattern is consistent. We believe that, in a world with about 50% women, this should not be the case. Although journalists naturally need to quote newsmakers who are men, they also have a certain amount of control over who they approach as sources. The Gender Gap Tracker relies on the same principles as fitness or goal-setting trackers: By quantifying and measuring regular progress, we hope to motivate news organizations to provide a more diverse set of voices in their reporting.

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Section: Gender Prediction and Gender Bias In Natural Language Processingmentioning

confidence: 99%

The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media

et al. 2021

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“…Gender affects myriad aspects of NLP, including corpora, tasks, algorithms, and systems Costa-jussà, 2019;Sun et al, 2019). For example, statistical gender biases are rampant in word embeddings (Jurgens et al, 2012;Bolukbasi et al, 2016;Caliskan et al, 2017;Garg et al, 2018;Zhao et al, 2018b;Basta et al, 2019;Chaloner and Maldonado, 2019;Du et al, 2019;Ethayarajh et al, 2019;Kaneko and Bollegala, 2019;Kurita et al, 2019;-including multilingual ones (Escudé Font and Costa-jussà, 2019;Zhou et al, 2019)-and affect a wide range of downstream tasks including coreference resolution (Zhao et al, 2018a;Cao and Daumé III, 2020;Emami et al, 2019), part-ofspeech and dependency parsing (Garimella et al, 2019), language modeling (Qian et al, 2019;Nangia et al, 2020), appropriate turn-taking classification (Lepp, 2019), relation extraction (Gaut et al, 2020), identification of offensive content (Sharifirad and Matwin, 2019;, and machine translation (Stanovsky et al, 2019;Hovy et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Multi-Dimensional Gender Bias Classification

Dinan¹,

Fan²,

Wu³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a novel, general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a new, crowdsourced evaluation benchmark. Distinguishing between gender bias along multiple dimensions enables us to train better and more fine-grained gender bias classifiers. We show our classifiers are valuable for a variety of applications, like controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness.

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“…By looking at the personal pronouns used by the speakers to describe themselves, our manual assignment instead is meant to account for the gender linguistic forms by which the speakers accept to be referred to in English (GLAAD, 2007), and would want their translations to conform to. We stress that gendered linguistic expressions do not directly map to speakers' self-determined gender identity (Cao and Daumé III, 2020). We therefore make explicit that throughout the paper, when talking about speakers' gender, we refer to their accepted linguistic expression of gender rather than their gender identity.…”

Section: Annotation Of Must-c With Speakers' Gender Informationmentioning

confidence: 99%

Breeding Gender-aware Direct Speech Translation Systems

Gaido¹,

Savoldi²,

Bentivogli

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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In automatic speech translation (ST), traditional cascade approaches involving separate transcription and translation steps are giving ground to increasingly competitive and more robust direct solutions. In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e.g. speaker's vocal characteristics) that is otherwise lost in the cascade framework. Although such ability proved to be useful for gender translation, direct ST is nonetheless affected by gender bias just like its cascade counterpart, as well as machine translation and numerous other natural language processing applications. Moreover, direct ST systems that exclusively rely on vocal biometric features as a gender cue can be unsuitable and potentially harmful for certain users. Going beyond speech signals, in this paper we compare different approaches to inform direct ST models about the speaker's gender and test their ability to handle gender translation from English into Italian and French. To this aim, we manually annotated large datasets with speakers' gender information and used them for experiments reflecting different possible real-world scenarios. Our results show that gender-aware direct ST solutions can significantly outperform strong -but gender-unaware -direct ST models. In particular, the translation of gender-marked words can increase up to 30 points in accuracy while preserving overall translation quality.

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Toward Gender-Inclusive Coreference Resolution

Cited by 61 publications

References 158 publications

The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media

The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media

Multi-Dimensional Gender Bias Classification

Breeding Gender-aware Direct Speech Translation Systems

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