Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of A frican- A merican E nglish (2022)

Abstract: 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 non-interpretable 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.

## introduction over the years, social media users have leveraged online conversational platforms to perpetually express themselves online. for example, african american english (aae) foot_0 , an english language variety is often heavily used on twitter @xcite . this dialect continuum is neither spoken by all african americans or individuals who identify as bipoc (black, indigenous, or people of color), nor is it spoken only by african americans or bipoc individuals @xcite . in some cases, aae, a low-resource language (lrl) may be the first (or dominant) language, rather than the second (or non-dominant) language of an english speaker. specifically, aae is a regional dialect continuum that consists of a distinct set of lexical 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) @xcite @xcite @xcite @xcite . in particular, @xcite states that aae possesses a morphologically invariant form of the verb that distinguishes between habitual action and currently occurring action, namely habitual be. for example, "the habitual be" experiment foot_2 by university of massachusetts amherst's janice jackson. however, aae is perceived to be "bad english" despite numerous studies by socio/raciolinguists and dialectologists in their attempts to quantify aae as a legitimized language @xcite @xcite . ## related work previous works regarding aae linguistic features have analyzed tasks such as unsupervised domain adaptation for aae-like language @xcite , detecting aae syntax @xcite , language identification @xcite , voice recognition and transcription @xcite , dependency parsing @xcite , dialogue systems @xcite , hate speech/toxic language detection and examining racial bias @xcite @xcite @xcite @xcite , and language generation @xcite . these central works are conclusive for highlighting systematic biases of natural language processing (nlp) systems when employing aae in common downstream tasks. although we mention popular works incorporating aae, this dialectal continuum has been largely ignored and underrepresented by the nlp community in comparison to mae. such lack of language diversity cases constitutes technological inequality to minority groups, for example, by african americans or bipoc individuals, and may intensify feelings of disenfranchisement due to monolingualism. we refer to this pitfall as the inconvenient truth i.e., "[if] the systems show discriminatory behaviors in the interactions, the user experience will be adversely affected." ## methodology in this section, we describe our approach to perform a preliminary study to validate the existence of predictive bias @xcite in pos models. we first introduce the pos tagging, and then propose two ml sequence models. ## operationalization of aae as an english language variety as (online) aae can incorporate non-standardized spellings and lexical items, there is an active need for a human-in-the-loop paradigm as humans provide various forms of feedback in different stages of workflow. this can significantly improve the model's performance, interpretability, explainability, and usability. therefore, crowd-sourcing to develop language technologies that consider who created the data will lead to the inclusion of diverse training data, and thus, decrease feelings of marginalization. for example, coraal 13 , is an online resource that features aal text data, recorded speech data, etc., into new and existing nlp technologies, aae speakers can extensively interact with current nlp language technologies. consequently, to quantitatively and qualitatively ensure fairness in nlp tools, artificial intelligence (ai) and nlp researchers need to go beyond evaluation measures, word definitions and word order to assess aae on a token-level to better understand context, culture and word ambiguities. we encourage both ai and nlp practitioners to prioritize collecting a set of relevant labeled training data with several examples of informal phrases, expressions, idioms, and regional-specific varieties. specifically, in models intended for broad use such as sentiment analysis by partnering with low-resource and di-alectal communities to develop impactful speech and language technologies for dialect continua such as aae to minimize further stigmatization of an already stigmatized minority group. ## conclusion throughout this work, we highlight the need to develop language technologies for such varieties, pushing back against potentially discriminatory practices (in many cases, discriminatory through oversight more than malice). our work calls for nlp researchers to consider both social and racial hierarchies sustained or intensified by current computational linguistic research. by shifting towards a human-in-the-loop paradigm to conduct deep multi-layered dialectal language analysis of aae to counter-attack erasure and several forms of biases such as selection bias, label bias, model overamplification, and semantic bias (see @xcite for definitions) in nlp. we hope our dynamic approach can encourage practitioners, researchers and developers for aae inclusive work, and that our contributions can pave the way for normalizing the use of a human-in-theloop paradigm both to obtain new data and create nlp tools to better comprehend underrepresented dialect continua and english language varieties. in this way, nlp community can revolutionize the ways in which humans and technology cooperate by considering certain demographic attributes such as culture, background, race and gender when developing and deploying nlp models.