Towards Better Inclusivity: A Diverse Tweet Corpus of English Varieties

TL;DR

This paper introduces a diverse Twitter corpus of 170,800 tweets from seven countries, annotated to reflect various English varieties, aiming to reduce bias in NLP tools by improving data representation of underrepresented English dialects.

Contribution

The paper presents a new annotated tweet dataset from multiple countries, focusing on underrepresented English varieties, and proposes a classification framework to measure standardness and linguistic differences.

Findings

Identified accuracy gaps in language identification for non-standard English varieties.

Created a large, regionally annotated tweet corpus to support bias reduction in NLP.

Highlighted the need for diverse data to improve NLP fairness and inclusivity.

Abstract

The prevalence of social media presents a growing opportunity to collect and analyse examples of English varieties. Whilst usage of these varieties was - and, in many cases, still is - used only in spoken contexts or hard-to-access private messages, social media sites like Twitter provide a platform for users to communicate informally in a scrapeable format. Notably, Indian English (Hinglish), Singaporean English (Singlish), and African-American English (AAE) can be commonly found online. These varieties pose a challenge to existing natural language processing (NLP) tools as they often differ orthographically and syntactically from standard English for which the majority of these tools are built. NLP models trained on standard English texts produced biased outcomes for users of underrepresented varieties. Some research has aimed to overcome the inherent biases caused by unrepresentative data through techniques like data augmentation or adjusting training models. We aim to address the issue of bias at its root - the data itself. We curate a dataset of tweets from countries with high proportions of underserved English variety speakers, and propose an annotation framework of six categorical classifications along a pseudo-spectrum that measures the degree of standard English and that thereby indirectly aims to surface the manifestations of English varieties in these tweets. Following best annotation practices, our growing corpus features 170,800 tweets taken from 7 countries, labeled by annotators who are from those countries and can communicate in regionally-dominant varieties of English. Our corpus highlights the accuracy discrepancies in pre-trained language identifiers between western English and non-western (i.e., less standard) English varieties. We hope to contribute to the growing literature identifying and reducing the implicit demographic discrepancies in NLP.