Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness

• URL: http://arxiv.org/abs/2406.09977v1
• Date: Fri, 14 Jun 2024 12:39:39 GMT
• Title: Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
• Authors: Maximilian Spliethöver, Sai Nikhil Menon, Henning Wachsmuth,
• Abstract summary: We present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.
• Score: 16.746758715820324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.

Related papers

• Dialetto, ma Quanto Dialetto? Transcribing and Evaluating Dialects on a Continuum [25.732397636695882]
  We measure speech-to-text performance on Italian dialects, and empirically observe a geographical performance disparity. This disparity correlates substantially (-0.5) with linguistic similarity to the highest performing dialect variety. We additionally leverage geostatistical methods to predict zero-shot performance at unseen sites, and find the incorporation of geographical information to substantially improve prediction performance.
  arXiv  Detail & Related papers  (2024-10-18T16:39:42Z)
• Collapsed Language Models Promote Fairness [88.48232731113306]
  We find that debiased language models exhibit collapsed alignment between token representations and word embeddings. We design a principled fine-tuning method that can effectively improve fairness in a wide range of debiasing methods.
  arXiv  Detail & Related papers  (2024-10-06T13:09:48Z)
• Exploring Diachronic and Diatopic Changes in Dialect Continua: Tasks, Datasets and Challenges [2.572144535177391]
  We critically assess nine tasks and datasets across five dialects from three language families (Slavic, Romance, and Germanic) We outline five open challenges regarding changes in dialect use over time, the reliability of dialect datasets, the importance of speaker characteristics, limited coverage of dialects, and ethical considerations in data collection. We hope that our work sheds light on future research towards inclusive computational methods and datasets for language varieties and dialects.
  arXiv  Detail & Related papers  (2024-07-04T15:38:38Z)
• Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations [15.394018604836774]
  We introduce a trial-and-demonstration (TnD) learning framework that incorporates three components: student trials, teacher demonstrations, and a reward conditioned on language competence. Our experiments reveal that the TnD approach accelerates word acquisition for student models of equal or smaller numbers of parameters. Our findings suggest that interactive language learning, with teacher demonstrations and student trials, can facilitate efficient word learning in language models.
  arXiv  Detail & Related papers  (2024-05-22T16:57:02Z)
• Modeling Orthographic Variation in Occitan's Dialects [3.038642416291856]
  Large multilingual models minimize the need for spelling normalization during pre-processing. Our findings suggest that large multilingual models minimize the need for spelling normalization during pre-processing.
  arXiv  Detail & Related papers  (2024-04-30T07:33:51Z)
• Task-Agnostic Low-Rank Adapters for Unseen English Dialects [52.88554155235167]
  Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English. By disentangling dialect-specific and cross-dialectal information, HyperLoRA improves generalization to unseen dialects in a task-agnostic fashion.
  arXiv  Detail & Related papers  (2023-11-02T01:17:29Z)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
  Figurative and metaphorical language are commonplace in discourse. It remains an open question to what extent modern language models can interpret nonliteral phrases. We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
  arXiv  Detail & Related papers  (2022-04-26T23:42:22Z)
• On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment [59.995385574274785]
  We show that, contrary to previous belief, negative interference also impacts low-resource languages. We present a meta-learning algorithm that obtains better cross-lingual transferability and alleviates negative interference.
  arXiv  Detail & Related papers  (2020-10-06T20:48:58Z)
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer [101.58431011820755]
  We study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations.
  arXiv  Detail & Related papers  (2020-05-02T04:34:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.