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Classist Tools: Social Class Correlates with Performance in NLP (2403.04445v1)

Published 7 Mar 2024 in cs.CL

Abstract: Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodemographic characteristics and language production and perception. But while there is strong evidence for socio-demographic characteristics in language, they are infrequently used in NLP. Age and gender are somewhat well represented, but Labov's original target, socioeconomic status, is noticeably absent. And yet it matters. We show empirically that NLP disadvantages less-privileged socioeconomic groups. We annotate a corpus of 95K utterances from movies with social class, ethnicity and geographical language variety and measure the performance of NLP systems on three tasks: LLMling, automatic speech recognition, and grammar error correction. We find significant performance disparities that can be attributed to socioeconomic status as well as ethnicity and geographical differences. With NLP technologies becoming ever more ubiquitous and quotidian, they must accommodate all language varieties to avoid disadvantaging already marginalised groups. We argue for the inclusion of socioeconomic class in future language technologies.

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Authors (4)
  1. Amanda Cercas Curry (18 papers)
  2. Giuseppe Attanasio (21 papers)
  3. Zeerak Talat (24 papers)
  4. Dirk Hovy (57 papers)
Citations (3)
View on Semantic Scholar

Summary

Exploring the Impact of Socioeconomic Status on NLP Performance

Introduction

NLP systems are indispensable tools in the modern digital landscape, offering capabilities ranging from LLMing and automatic speech recognition to grammar correction. Developing inclusive NLP technologies necessitates understanding and addressing performance disparities across diverse demographic groups. Recognizing the significant but often overlooked impact of socioeconomic status (SES) on language use, Amanda Cercas Curry, Giuseppe Attanasio, Zeerak Talat, and Dirk Hovy investigate the ways NLP tools perform across different SES groups. Their paper, "Classist Tools: Social Class Correlates with Performance in NLP," sheds light on the empirical evidence of how less-privileged socioeconomic groups are disadvantaged by current NLP technologies.

Dataset and Methodology

The research team embarked on a comprehensive paper involving the annotation of 95K utterances from movie scripts, categorizing them based on social class, ethnicity, and geographical language variety. This novel dataset provided a foundation for analyzing NLP system performance across three critical tasks: LLMing, automatic speech recognition, and grammar error correction.

Utilizing popular television shows and movies allowed for the ethically responsible collection of data representing a spectrum of socioeconomic statuses, ethnic backgrounds, and dialects. Shows were selected to cover a balanced representation, including both dominant and marginalized groups across different SES strata and geographical regions (primarily the US and UK).

Findings and Discussion

Socioeconomic Status and Language Variation

The paper confirms that socioeconomic status significantly impacts linguistic expression, as echoed in past sociolinguistic research. This impact manifests in various linguistic features, including lexicon, syntax, and style, which arguably should be considered in the design and deployment of NLP systems.

Performance Disparities in NLP Tasks

The empirical analysis across different NLP tasks reveals significant performance disparities attributable to differences in socioeconomic status, as well as ethnicity and geographical language variations. For instance, automatic speech recognition systems demonstrated higher error rates for lower SES groups and non-standard dialects. Similarly, LLMs exhibited higher perplexity scores—indicating lower "expectedness" or acceptability—for utterances attributed to lower SES, suggesting an inherent bias towards more privileged sociolects.

Implications for Fairness in NLP

These findings prompt critical reflection on the inclusivity and fairness of NLP technologies. As NLP systems become increasingly embedded in everyday digital interactions, there is a pressing need to ensure that these technologies do not perpetuate or exacerbate existing social inequalities. The research articulates a call to action for incorporating socio-demographic characteristics, such as socioeconomic status, into the design, development, and evaluation of NLP systems.

Concluding Thoughts

The paper conducted by Curry et al. represents an important step towards understanding and mitigating biases in NLP systems related to socioeconomic status. By highlighting the performance disparities and their potential implications, the research underscores the importance of developing NLP technologies that are inclusive and equitable across all social strata. Looking forward, the research paves the way for future investigations into socio-demographic factors in NLP, advocating for a more holistic approach to inclusivity in technology design and application.

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