Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

Rabinovich, Ella; Gonen, Hila; Stevenson, Suzanne

doi:10.18653/v1/2020.coling-main.454

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…Finally, we report the lexicon-based evaluation results in Fig 6 and Fig 7. Unsurprisingly, we observe lower dominance levels for the non-white and female attributes compared to white and male, a finding previously uncovered in modern texts (Field and Tsvetkov, 2019;Rabinovich et al, 2020). While Fig 7 indicates that the level of dominance associated with these attributes raised over time, a noticeable disparity to white males remains.…”

Section: Lexicon Evaluationsupporting

confidence: 57%

Measuring Intersectional Biases in Historical Documents

Borenstein,

Stanczak,

Rolskov

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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Data-driven analyses of biases in historical texts can help illuminate the origin and development of biases prevailing in modern society. However, digitised historical documents pose a challenge for NLP practitioners as these corpora suffer from errors introduced by optical character recognition (OCR) and are written in an archaic language. In this paper, we investigate the continuities and transformations of bias in historical newspapers published in the Caribbean during the colonial era (18th to 19th centuries). Our analyses are performed along the axes of gender, race, and their intersection. We examine these biases by conducting a temporal study in which we measure the development of lexical associations using distributional semantics models and word embeddings. Further, we evaluate the effectiveness of techniques designed to process OCRgenerated data and assess their stability when trained on and applied to the noisy historical newspapers. We find that there is a trade-off between the stability of the word embeddings and their compatibility with the historical dataset. We provide evidence that gender and racial biases are interdependent, and their intersection triggers distinct effects. These findings align with the theory of intersectionality, which stresses that biases affecting people with multiple marginalised identities compound to more than the sum of their constituents. https://github.com/copenlu/i ntersectional-bias-pbw

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Section: Lexicon Evaluationsupporting

confidence: 57%

Measuring Intersectional Biases in Historical Documents

Borenstein,

Stanczak,

Rolskov

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

View full text Add to dashboard Cite

show abstract

“…Additionally, due to the joke targeting men, there is the possibility for some men to be offended at the assumptions that it is making, and it is known that gender generally affects the perception of certain types of jokes such as these (Lawless et al, 2020). Furthermore, a large body of work has investigated the different tendencies of particular genders towards different patterns of language use, which in some cases may also affect the perception of different language types (Coates, 2016;Rabinovich et al, 2020). Finally, Example 3 (Chomsky, 1957) is an instance of an incongruous sentence which is semantically nonsensical (yet grammatically valid), where this incongruity does not give rise to humour (in the general case).…”

Section: Humourmentioning

confidence: 99%

The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation

Loakman,

Maladry,

Lin

2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Human evaluation is often considered to be the gold standard method of evaluating a Natural Language Generation system. However, whilst its importance is accepted by the community at large, the quality of its execution is often brought into question. In this position paper, we argue that the generation of more esoteric forms of language -humour, irony and sarcasm -constitutes a subdomain where the characteristics of selected evaluator panels are of utmost importance, and every effort should be made to report demographic characteristics wherever possible, in the interest of transparency and replicability. We support these claims with an overview of each language form and an analysis of examples in terms of how their interpretation is affected by different participant variables. We additionally perform a critical survey of recent works in NLG to assess how well evaluation procedures are reported in this subdomain, and note a severe lack of open reporting of evaluator demographic information, and a significant reliance on crowdsourcing platforms for recruitment.

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“…3 , and United States Congressional dataset (US Congr.) 4 (Rabinovich et al, 2020;Sandhaus, 2008;Beelen et al, 2017;Gentzkow et al, 2018). A summary of statistics for these corpora is shown in Table 1.…”

Section: Diachronic Text Corporamentioning

confidence: 99%

A diachronic evaluation of gender asymmetry in euphemism

Kapron-King

2021

Preprint

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The use of euphemisms is a known driver of language change. It has been proposed that women use euphemisms more than men. Although there have been several studies investigating gender differences in language, the claim about euphemism usage has not been tested comprehensively through time. If women do use euphemisms more, this could mean that women also lead the formation of new euphemisms and language change over time. Using four large diachronic text corpora of English, we evaluate the claim that women use euphemisms more than men through a quantitative analysis. We assembled a list of 106 euphemism-taboo pairs to analyze their relative use through time by each gender in the corpora. Contrary to the existing belief, our results show that women do not use euphemisms with a higher proportion than men. We repeated the analysis using different subsets of the euphemism-taboo pairs list and found that our result was robust. Our study indicates that in a broad range of settings involving both speech and writing, and with varying degrees of formality, women do not use or form euphemisms more than men.

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Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

Cited by 4 publications

References 31 publications

Measuring Intersectional Biases in Historical Documents

Measuring Intersectional Biases in Historical Documents

The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation

A diachronic evaluation of gender asymmetry in euphemism

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