Datasets:

WINGNUS
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ACL-OCL

	{
	"paper_id": "2022",
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	"date_generated": "2023-01-19T03:35:25.816502Z"
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	"title": "Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English",
	"authors": [
	{
	"first": "Jamell",
	"middle": [],
	"last": "Dacon",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Michigan State University East Lansing",
	"location": {
	"region": "MI",
	"country": "USA"
	}
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	"email": "[email protected]"
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	"year": "",
	"venue": null,
	"identifiers": {},
	"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 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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	"paper_id": "2022",
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	"abstract": [
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	"text": "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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	"section": "Abstract",
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	"text": "Over the years, social media users have leveraged online conversational platforms to perpetually express themselves online. For example, African American English (AAE) 1 , an English language variety is often heavily used on Twitter (Field et al., 2021; Blodgett et al., 2020) . 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 (Field et al., 2021; Bland-Stewart, 2005) . 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.",
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	"section": "Introduction",
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	"text": "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) (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. For example, \"the habitual be\" experiment 2 by University of Massachusetts Amherst's Janice Jackson.",
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	"start": 335,
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	"start": 368,
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	"text": "Bland-Stewart, 2005;",
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	"start": 389,
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	"start": 402,
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	"text": "Blodgett et al., 2020;",
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	"start": 425,
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "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 (Baugh, 2008; Field et al., 2021; Bland-Stewart, 2005; Labov, 1975) .",
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	"start": 189,
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	"start": 209,
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "\" [T] he common misconception [is] that language use has primarily to do with words and what they mean. It doesn't. It has primarily to do with people and what they mean.\" -Clark and Schober (1992) Recently, online AAE has influenced the generation of resources for AAE-like text for natural language (NLP) and corpus linguistic tasks e.g., partof-speech (POS) tagging (J\u00f8rgensen et al., 2016; Blodgett et al., 2018) , language generation (Groenwold et al., 2020) and automatic speech recognition (Dorn, 2019; Tatman and Kasten, 2017) . POS tagging is a token-level text classification task where each token is assigned a corresponding word category label (see Table 1 ). It is an enabling tool for NLP applications such as a syntactic parsing, named entity recognition, corpus linguistics, etc. In this work, we incorporate a human-in-the-loop paradigm by directly involving affected (user) communities to understand context and word ambigu-MAE Input I have never done this before Output (I, <PRP>), (have, <VBP>), (never, <RB>), (done, <VBN>), (that, <IN>), (before, <IN>)",
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	"text": "(1992)",
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	"start": 394,
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	"text": "Blodgett et al., 2018)",
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	"start": 497,
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	"text": "(Dorn, 2019;",
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	"start": 661,
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	"text": "Table 1",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "Input I aint neva did dat befo Output (I, <PRP>), (aint, < VBP >), (neva, < NN >), (did, <VBD>)(dat, < JJ >), (befo, < NN >) Table 1 : An illustrative example of POS tagging of semantically equivalent sentences written in MAE and AAE. Each blue and red highlight corresponds to linguistics features of AAE lexical items, and their misclassified NLTK (inferred) tags, respectively.",
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	"start": 125,
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	"section": "AAE",
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	"text": "ities in an attempt to study dialectal language inclusivity in NLP language technologies that are generally designed for dominant language varieties. Dacon and Liu (2021) 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). Next, we incorporate a human-inthe-loop paradigm by recruiting 20 crowd-sourced diglossic annotators to evaluate AAE language variety (see Table 2 ). Finally, we conclude by expanding on the need for dialectal language inclusivity.",
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	"section": "AAE",
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	"text": "Previous works regarding AAE linguistic features have analyzed tasks such as unsupervised domain adaptation for AAE-like language (J\u00f8rgensen et al., 2016) , detecting AAE syntax (Stewart, 2014) , language identification (Blodgett and O'Connor, 2017), voice recognition and transcription (Dorn, 2019) , dependency parsing (Blodgett et al., 2018) , dialogue systems (Liu et al., 2020) , hate speech/toxic language detection and examining racial bias (Sap et al., 2019; Halevy et al., 2021; Xia et al., 2020; Davidson and Bhattacharya, 2020; Zhou et al., 2021; Mozafari et al., 2020; Xu et al., 2021; Koenecke et al., 2020) , and language generation (Groenwold et al., 2020) . These central works are conclusive for highlighting systematic biases of natural language processing (NLP) systems when employing AAE in common downstream tasks.",
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	"text": "Xia et al., 2020;",
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	"text": "Zhou et al., 2021;",
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	"text": "Mozafari et al., 2020;",
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	"ref_id": "BIBREF38"
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	"text": "Koenecke et al., 2020)",
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	"section": "Related Work",
	"sec_num": "2"
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	"text": "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.\" -Liu et al. 2020Therefore, we define fairness as the model's ability to correctly predict each tag while performing zeroshot transfer via dialectal language inclusivity. Moreover, these aforementioned works do not discuss nor reflect on the \"role of the speech and language technologies in sustaining language use\" (Labov, 1975; Bird, 2020; Blodgett et al., 2020) as, \"... models are expected to make predictions with the semantic information rather than with the demographic group identity information\" -Zhang et al. (2020).",
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	"section": "Related Work",
	"sec_num": "2"
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	"text": "Interactions with everyday items is increasingly mediated through language, yet systems have limited ability to process less-represented dialects such as AAE. For example, a common AAE phrase, \"I had a long ass day\" would receive a lower sentiment polarity score because of the word \"ass\", a (noun) term typically classified as offensive; however, in AAE, this term is often used as an emphatic, cumulative adjective and perceived as non-offensive. Motivation: We want to test our hypothesis that training each model on correctly tagged AAE language features will improve the model's performance, interpretability, explainability, and usability to reduce predictive bias.",
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	"section": "Related Work",
	"sec_num": "2"
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	"text": "We collect 3000 demographically-aligned African American (AA) tweets possessing an average of 7 words per tweet from the publicly available Twit-terAAE corpus by Blodgett et al. (2016) . Each tweet is accompanied by inferred geolocation topic model probabilities from Twitter + Census demographics and word likelihoods to calculate demographic dialect proportions. We aim to minimize (linguistic) discrimination by sampling tweets that possess over 99% confidence to develop \"fair\" NLP tools that are originally designed for dominant language varieties by integrating non-standardized varieties. More information about the TwitterAAE dataset, including its statistical information, annotation process, and the link(s) to downloadable versions can be found in Appendix A.",
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	"text": "Blodgett et al. (2016)",
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	"section": "Dataset",
	"sec_num": "3.1"
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	"text": "As it is common for most words on social media to be plausibly semantically equivalent, we denoise each tweet as tweets typically possess unusual spelling patterns, repeated letter, emoticons and emojis 4 . We replace sequences of multiple repeated letters with three repeated letters (e.g., Hmmmmmmmm \u2192 Hmmm), and remove all punctuation, \"@\" handles of users and emojis. Essentially, we aim to denoise each tweet only to capture non-standard spellings and lexical items more efficiently.",
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	"section": "Preprocessing",
	"sec_num": "3.2"
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	"text": "First, we employ off-the-shelf taggers such as spacy 5 and TwitterNLP 6 ; however, the Natural Language Toolkit (NLTK) (Loper and Bird, 2002) provides a more fine-grained Penn Treebank Tagset (PTB) 7 along with evaluation metrics per tag such as F1 score. Next, we focus on aggregating the appropriate tags by collecting and manuallyannotating tags from AAE/slang-specific dictionaries to assist the AMT annotators, and later we contrast these aggregated tags with inferred NLTK PTB inferred tags. In Figure 1 , we display NLTK inferred and manually-annotated AAE tags from k = 300 randomly sampled tweets.",
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	"section": "Annotation",
	"sec_num": "3.3"
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	{
	"text": "\u2022 The Online Slang Dictionary (American, English, and Urban slang) 8 -created in 1996, this is the oldest web dictionary of slang words, neologisms, idioms, aphorisms, jargon, informal speech, and figurative usages. This dicto create pictorial icons to display an emotion or sentiment (e.g., \";)\" \u21d2 winking smile), while emojis are small text-like pictographs of faces, objects, symbols, etc. 5 https://spacy.io 6 https://github.com/ianozsvald/ ark-tweet-nlp-python tionary possesses more than 24,000 real definitions and tags for over 17,000 slang words and phrases, 600 categories of meaning, word use mapping and aids in addressing lexical ambiguity.",
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	"section": "Annotation",
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	"text": "\u2022 Word Type 9 -an open source POS focused dictionary of words based on the Wiktionary 10 project by Wikimedia 11 . Researchers have parsed Wiktionary and other sources, including real definitions and categorical POS word use cases necessary to address the issue of lexical, semantic and syntactic ambiguity.",
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	"section": "Annotation",
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	"text": "After an initial training of the AMT annotators, we task each annotator to annotate each tweet with the appropriate POS tags. Then, as a calibration study we attempt to measure the interannotator agreement (IAA) using Krippendorff's \u03b1. By using NLTK's (Loper and Bird, 2002) nltk.metrics.agreement, we calculate a Krippendorf's \u03b1 of 0.88. We did not observe notable distinctions in annotator agreement across the individual tweets. We later randomly sampled 300 annotated tweets and recruit 20 crowd-sourced annotators to evaluate AAE language variety. To recruit 20 diglossic annotators 12 , we created a volunteer questionnaire with annotation guildlines, and released it on LinkedIn. The full annotation guildlines can be found in Appendix B. Each recruited annotator is tasked to judge sampled tweets and list their MAE equivalents to examine contextual differences of simple, deterministic morphosyntactic substitutions of dialect-specific vocabulary in standard English or MAE texts-a reverse study to highlight several varieties of AAE (see Table 2 ).",
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	"text": "(Loper and Bird, 2002)",
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	"section": "Human Evaluation",
	"sec_num": "3.4"
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	{
	"text": "In this section, we describe our approach to perform a preliminary study to validate the existence of predictive bias (Elazar and Goldberg, 2018; Shah et al., 2020) in POS models. We first introduce the POS tagging, and then propose two ML sequence models.",
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	"section": "Methodology",
	"sec_num": "4"
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	"text": "We consider POS tagging as it represents word syntactic categories and serves as a pre-annotation tool for numerous downstream tasks, especially for non-standardized English language varieties such as AAE (Zampieri et al., 2020) . Common tags include prepositions, adjective, pronoun, noun, adverb, verb, interjection, etc., where multiple POS tags can be assigned to particular words due to syntactic structural patterns. This can also lead to misclassification of non-standardized words that do not exist in popular pre-trained NLP models.",
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	"text": "(Zampieri et al., 2020)",
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	"section": "Part-of-Speech (POS) Tagging",
	"sec_num": "4.1"
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	"text": "We propose to implement two well known sequence modeling algorithms, namely a Bidirectional Long Short Term Memory (Bi-LSTM) network, a deep neutral network (DNN) (Hochreiter and Schmidhuber, 1997; Graves and Schmidhuber, 2005 ) that has been used for POS tagging (Ling et al., 2015; Plank et al., 2016) , and a Conditional Random Field (CRF) (Lafferty et al.) typically used to identify entities or patterns in texts by exploiting previously learned word data.",
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	"text": "(Hochreiter and Schmidhuber, 1997;",
	"ref_id": "BIBREF19"
	},
	{
	"start": 198,
	"end": 226,
	"text": "Graves and Schmidhuber, 2005",
	"ref_id": "BIBREF14"
	},
	{
	"start": 264,
	"end": 283,
	"text": "(Ling et al., 2015;",
	"ref_id": "BIBREF26"
	},
	{
	"start": 284,
	"end": 303,
	"text": "Plank et al., 2016)",
	"ref_id": "BIBREF30"
	},
	{
	"start": 343,
	"end": 360,
	"text": "(Lafferty et al.)",
	"ref_id": null
	}
	],
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	"section": "Models",
	"sec_num": "4.2"
	},
	{
	"text": "Taggers: First, we use NLTK (Loper and Bird, 2002) for automatic tagging; then, we pre-define a feature function for our CRF model where we optimized its L1 and L2 regularization parameters to 0.25 and 0.3, respectively. Later, we train our Bi-LSTM network for 40 epochs with an Adam optimizer, and a learning rate of 0.001. Note that each model would be accompanied by error analysis for a 70-30 split of the data with 5-fold cross-validation to obtain model classification reports, for metrics such as precision, recall and F1-score.",
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	{
	"start": 28,
	"end": 50,
	"text": "(Loper and Bird, 2002)",
	"ref_id": "BIBREF28"
	}
	],
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	"sec_num": "4.2"
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	{
	"text": "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.",
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	"section": "Operationalization of AAE as an English Language Variety",
	"sec_num": "5"
	},
	{
	"text": "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 Shah et al. (2020) for definitions) in NLP.",
	"cite_spans": [],
	"ref_spans": [],
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	"section": "Conclusion",
	"sec_num": "6"
	},
	{
	"text": "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.",
	"cite_spans": [],
	"ref_spans": [],
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	"section": "Conclusion",
	"sec_num": "6"
	},
	{
	"text": "All authors must warrant that increased model performance for non-standard varieties such as underrepresented dialects, non-standard spellings or lexical items in NLP systems can potentially enable automated discrimination. In this work, we solely attempt to highlight the need for dialectal inclusivity for the development of impactful speech and language technologies in the future, and do not intend for increased feelings of marginalization of an already stigmatized community. mixed-membership demographic language model which calculates demographic dialect proportions for a text accompanied by a race attribute-African America, Hispanic, Other, and White in that order. The race attribute is annotated by a jointly inferred probabilistic topic model based on the geolocation information of each user and tweet. Given that geolocation information (residence) is highly associated with the race of a user, the model can make accurate predictions. However, there a a low number messages that possess a posterior probabilities of NaN as these are messages that have no in-vocabulary words under the model.",
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	"section": "Limitations And Ethical Considerations",
	"sec_num": "7"
	},
	{
	"text": "You will be given demographically-aligned African American tweets, in which we refer to these tweets as sequences. As a dominant AAE speaker, who identifies as bi-dialectal, your task is to correctly identify the context of each word in a given sequence in hopes to address the issues of lexical, semantic and syntactic ambiguity.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "1. Are you a dominant AAE speaker?",
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	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "2. If you responded \"yes\" above, are you bidialectal?",
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	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "3. If you responded \"yes\", given a sequence, have you ever said, seen or used any of these words given the particular sequence?",
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	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "4. Given a sequence, what are the SAE equivalents to the identified non-SAE terms?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "5. For morphological and phonological (dialectal) purposes, are these particular words spelt how would you say or use them?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "6. If you responded \"no\" above, can you provide a different spelling along with its SAE equivalent?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "B.1 Annotation Protocol 1. What is the context of each word given the particular sequence?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "2. Given NLTK's Penn Treebank Tagset 15 , what is the most appropriate POS tag for each word in the given sequence?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "15 https://www.guru99.com/ pos-tagging-chunking-nltk.html B.2 Human evaluation of POS tags Protocol 1. Given the tagged sentence, are there any misclassified tags?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "2. If you responded \"yes\" above, can you provide a different POS tag, and state why it is different?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "B Annotator Annotation Guidelines",
	"sec_num": null
	},
	{
	"text": "In this section we present a few examples of simple, deterministic phonological and morphological language features or current variable rules which highlight several regional varieties of AAE which typically attain misclassified POS tags. Please note that a more exhaustive list of these rules is still being constructed as this work is still ongoing. Below are a few variable cases (MAE \u2192 AAE), some of which may have been previously shown in Table 2 :",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 444,
	"end": 451,
	"text": "Table 2",
	"ref_id": "TABREF2"
	}
	],
	"eq_spans": [],
	"section": "C Variable Rules Examples",
	"sec_num": null
	},
	{
	"text": "https://www.umass.edu/synergy/fall98/ ebonics3.html",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	},
	{
	"text": "A HIT approval rate \u2265 95% was used to select 5 bidialectal AMT annotators between the ages of 18 -55, and completed > 10,000 HITs and located within the United States.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	},
	{
	"text": "Emoticons are particular textual features made of punctuation such as exclamation marks, letters, and/or numbers",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	},
	{
	"text": "https://wordtype.org/ 10 https://www.wiktionary.org 11 https://www.wikimedia.org 12 Note that we did not collect certain demographic information such as gender or race, only basic demographics such as age (18-55 years), state and country of residence.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	},
	{
	"text": "https://oraal.uoregon.edu/coraal",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	},
	{
	"text": "http://slanglab.cs.umass.edu/ TwitterAAE/",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	}
	],
	"back_matter": [
	{
	"text": "The authors would like to thank Shaylnn L.A. Crum-Dacon, Serena Lotreck, Brianna Brown and Kenia Segura Ab\u00e1, Jyothi Kumar and Shin-Han Shiu for their support and the anonymous reviewers for their constructive comments.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Acknowledgements",
	"sec_num": "8"
	}
	],
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