Linguistic discrimination

Goebl, Hans; Nelde, Peter H.; Starý, Zdeněk; Wölck, Wolfgang

doi:10.1515/9783110132649.1.6.709

Cited by 7 publications

(13 citation statements)

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“…Stories can be powerful tools of persuasion and expression, and richer methods that draw from both NLP and HCI can raise new questions and open up new directions. items, some of which have distinct semantic meanings, and may possess different syntactic structures/patterns than in Mainstream American English (MAE) (e.g., differentiating habitual be and non-habitual be usage) (Stewart, 2014;Dorn, 2019;Jones, 2015;Field et al, 2021;Bland-Stewart, 2005;Baugh, 2008;Blodgett et al, 2020;Labov, 1975). In particular, Green (2002) states that AAE possesses a morphologically invariant form of the verb that distinguishes between habitual action and currently occurring action, namely habitual be.…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing

2022

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An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one that does not scale well. Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour to quickly build or extend a taxonomy to better fit information needs.

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Section: Discussionmentioning

confidence: 99%

Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing

2022

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“…Input I aint neva did dat befo Output (I, <PRP>), (aint, < VBP >), (neva, < NN >), (did, <VBD>)(dat, < JJ >), (befo, < NN >) To address these issues, we aim to empirically study predictive bias (see Swinton (1981) for definition) i.e., if POS tagger models make predictions dependent on demographic language features, and attempt a dynamic approach in data-collection of non-standard spellings and lexical items. To examine the behaviors of AAE speakers and their language use, we first collect variable (morphological and phonological) rules of AAE language features from literature (Labov, 1975;Bailey et al, 1998;Green, 2002;Bland-Stewart, 2005;Stewart, 2014;Blodgett et al, 2016;Elazar and Goldberg, 2018;Baugh, 2008;Green, 2014) (see Appendix C). Then, we employ 5 trained sociolinguist Amazon Mechanical Turk (AMT) annotators 3 who identify as bi-dialectal dominant AAE speakers to address the issue of lexical, semantic and syntactic ambiguity of tweets (see Appendix B for annotation guidelines).…”

Section: Aaementioning

confidence: 99%

“…Specifically, AAE is a regional dialect continuum that consists of a distinct set of lexical 1 A dialectal continuum previously known as Northern Negro English, Black English Vernacular (BEV), Black English, African American Vernacular English (AAVE), African American Language (AAL), Ebonics, and Non-standard English (Labov, 1975;Bailey et al, 1998;Green, 2002Green, , 2014Baugh, 2008;Bland-Stewart, 2005;King, 2020). It is often referred to as African American Language (AAL) and African American English (AAE).…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we use the denotation AAE. items, some of which have distinct semantic meanings, and may possess different syntactic structures/patterns than in Mainstream American English (MAE) (e.g., differentiating habitual be and non-habitual be usage) (Stewart, 2014;Dorn, 2019;Jones, 2015;Field et al, 2021;Bland-Stewart, 2005;Baugh, 2008;Blodgett et al, 2020;Labov, 1975). In particular, Green (2002) states that AAE possesses a morphologically invariant form of the verb that distinguishes between habitual action and currently occurring action, namely habitual be.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English

Dacon¹

2022

Preprint

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Currently, natural language processing (NLP) models proliferate language discrimination leading to potentially harmful societal impacts as a result of biased outcomes. For example, part-of-speech taggers trained on Mainstream American English (MAE) produce noninterpretable results when applied to African American English (AAE) as a result of language features not seen during training. In this work, we incorporate a human-in-the-loop paradigm to gain a better understanding of AAE speakers' behavior and their language use, and highlight the need for dialectal language inclusivity so that native AAE speakers can extensively interact with NLP systems while reducing feelings of disenfranchisement.

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“…Linguistic discrimination has adversely affected the lives of marginalized populations for centuries, including racially marginalized groups in the United States. In spite of extensive research on linguistic discrimination (Baugh, 2008), many NLP systems inherit the linguistic biases that exist between humans. For example, preliminary studies into the performance of automatic speech recognition (ASR) systems uncovered a performance bias against African American speakers ( Tatman and Kasten, 2017;Dorn, 2019).…”

Section: Introductionmentioning

confidence: 99%

Disambiguation of morpho-syntactic features of African American English -- the case of habitual be

Santiago¹,

Martin²,

Moeller³

et al. 2022

Preprint

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Recent research has highlighted that natural language processing (NLP) systems exhibit a bias against African American speakers. The bias errors are often caused by poor representation of linguistic features unique to African American English (AAE), due to the relatively low probability of occurrence of many such features in training data. We present a workflow to overcome such bias in the case of habitual "be". Habitual "be" is isomorphic, and therefore ambiguous, with other forms of "be" found in both AAE and other varieties of English. This creates a clear challenge for bias in NLP technologies. To overcome the scarcity, we employ a combination of rulebased filters and data augmentation that generate a corpus balanced between habitual and non-habitual instances. With this balanced corpus, we train unbiased machine learning classifiers, as demonstrated on a corpus of AAE transcribed texts, achieving .65 F 1 score disambiguating habitual "be".

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Linguistic discrimination

Cited by 7 publications

References 0 publications

Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing

Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing

Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English

Disambiguation of morpho-syntactic features of African American English -- the case of habitual be

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