With Prejudice to None: A Few-Shot, Multilingual Transfer Learning Approach to Detect Social Bias in Low Resource Languages (2023)

Abstract: In this paper, we describe our work on social bias detection in a low-resource multilingual setting in which the languages are from two very divergent families- Indo-European (English, Hindi, and Italian) and Altaic (Korean). Currently, the majority of the social bias datasets available are in English and this inhibits progress on social bias detection in low-resource languages. To address this problem, we introduce a new dataset for social bias detection in Hindi and investigate multilingual transfer learning using publicly available English, Italian, and Korean datasets. The Hindi dataset contains 9k social media posts annotated for (i) binary bias labels (bias/neutral), (ii) binary labels for sentiment (positive/negative), (iii) target groups for each bias category, and (iv) rationale for annotated bias labels (a short piece of text). We benchmark our Hindi dataset using different multilingual models, with XLM-R achieving the best performance of 80.8 macro-F1 score. Our results show that the detection of social biases in resource-constrained languages such as Hindi and Korean may be improved with the use of a similar dataset in English. We also show that translating all datasets into English does not work effectively for detecting social bias, since the nuances of source language are lost in translation. All the scripts and datasets utilized in this study will be publicly available.

## introduction social media has become one of the most important sources of information for users in recent years @xcite . the rise in the use of social media platforms @xcite , combined with the ease of access, allows unrestricted flow of content containing social biases, and stereotypes on such sites. furthermore, inequalities in socialmedia use exist across countries and regions as a result of various societal norms, cultures, and histories. thus, prejudice and societal biases vary across cultures. motivation: there has been a lot of focus on how to identify social bias either in data or model in recent years. this is because it is crucial that the systems we create do not encourage pre-existing prejudices. while there has been some study of this topic, it has largely been limited to high-resource languages like english @xcite . despite the fact that social bias is inextricably linked to cultural and linguistic characteristics of the language, non-english datasets @xcite @xcite are limited, hindering the development of social bias identification in other languages. in this work, we are expanding the bias detection task from english language to non-english languages. the results show that large datasets are not always required to develop efficient methods for identifying social bias in these resource-constrained languages. we intend to address the identification of social bias in the hindi language. with 602 million active speakers foot_0 , hindi is the world's third most spoken language. despite the fact that a sizable proportion of these folks choose to communicate online in devanagari foot_1 (script for hindi), there has been essentially no research on social bias detection in hindi. @xcite focuses on social bias detection in code-mixed and transliterated hindi language and @xcite releases a social bias benchmarking dataset using indian english. although both of these datasets are focusing on social biases in the indian context, none of them are in the hindi language. in this paper, we present our manually annotated dataset for social bias detection in hindi. we have annotated ∼ 9𝑘 online social media posts with multiple categorical labels. we empirically investigate the impact of languages from two different language families on the downstream task of social bias detection. further, we show that for bias detection, translating all datasets into english does not perform well. our contributions are: the rest of the paper is organized as follows: related works are discussed in section 2. section 3 gives insight into our dataset, terminologies, and annotation process. the methodologies and experiments are discussed in section 4. detailed error analysis is presented in section 5 followed by the concluding remarks, and the discussion on future works in section 6. ## related works the presence of social bias in language representations is mostly caused by the undesired and skewed associations within the training data. given the growing social effect of nlp applications, studying these undesired relationships is paramount @xcite . the initial attempts to tackle this issue focused on measuring and mitigating gender biases from word embeddings @xcite @xcite @xcite . additionally, multiple works have explored solutions to identify race, and religion bias in word embeddings @xcite . many subsequent works @xcite @xcite have also focused on contextualized language representation from models like bert for bias detection. more recently, many datasets @xcite have been created to measure social biases like gender, race, profession, religion, age, and so on in language models. @xcite has reported that these datasets lack clear definitions and have ambiguities and inconsistencies in annotations. researchers have also investigated the presence of biases in models trained for various nlp tasks like machine translation @xcite , question answering @xcite , and coreference resolution @xcite . there have been a lot of notable efforts towards identifying data bias in the problem of hate speech and offensive languages detection @xcite @xcite . @xcite has discussed unintended bias in hate speech detection models for identity terms like islam, lesbian, bisexual, etc. recent studies have also investigated the usefulness of counter-factual data augmentation @xcite @xcite to reduce the effect of unintended bias in these tasks. however, most of the bias detection and mitigation research is in english and has focused on western culture. few recent works have explored the issue of social bias in languages such as arabic, italian, spanish, french, and korean @xcite @xcite @xcite multilingual models acquire cross-lingual knowledge through the sharing of layers that allow for the alignment of representations across languages @xcite . in general, the low-resource languages in a multilingual framework benefit from the existence of other languages @xcite . @xcite explores the multilingual transfer learning between english and italian for hate speech and stereotype detection tasks. we study the effectiveness of multilingual transfer learning (also in few-shot setting) for four different languages. in the following section, we provide a comprehensive discussion of our annotated dataset, delving into its details, the definitions of each categorical label, accompanied by relevant examples, and an overview of the annotation process. ## hindi social bias dataset we have constructed this dataset foot_2 intending to explore the identity-related social prejudices and stereotyping in hindi from social media platforms such as instagram, facebook, twitter, etc. the major part of the dataset was initially developed as part of constraint-2021 foot_3 @xcite which focuses on hostility detection in lowresource regional languages. we augment this dataset with 994 more twitter posts which are scraped using twitter api foot_4 . we scraped the tweets using some keywords relating to the indian peninsula, such as, adivasi, dalit, dowry, child labor, casteism, farmer-protest, muslim, islam, hindu, hinduism, article15, article370, jain, poverty, sikh. for scraping we use both english and corresponding hindi keywords. after collecting all the tweets, we use language identification models to filter hindi tweets. after that we again filer the tweets based on the likes and retweet count. the tweets with minimum 100 likes and retweets are used for annotation purposes. the tweets were collected between january 1, 2021 and november 30, 2022. we follow the hierarchical annotation scheme by @xcite and annotate each post for (1) the presence of social bias in it, (2) the category of social bias, (3) specific identity group(s) if the post holds prejudice against/towards it, and (4) sentiment for each post. we also ask the annotators to ground their judgment by mentioning the rationale behind the social bias specified in the post. ## experiments this section describes all the experimental configurations. the training methodology is detailed in the appendix a. our experiments focus on predicting the presence of bias and its categories at the sentence level. we investigate multilingual transfer learning to measure the extent of task generalization across languages. dataset: to explore multi-lingual transfer learning we use our annotated dataset in hindi and publicly available english @xcite , italian @xcite , korean @xcite datasets. english dataset (stereoset) has posts collected after curating a set of target terms from wikidata triplets. for each target term, there are three associated sentences corresponding to stereotypical, anti-stereotypical, and unrelated associations. we disregard the anti-stereotype associations as many of them lack relevancy and veracity @xcite . the dataset was created to assess bias in four categories: gender, race, religion, and profession. as the entire dataset is not publicly available, we use a portion of it that is. the korean dataset was constructed using the comments from entertainment, news platforms. the dataset has two major bias labels: gender bias and other bias, which takes into account prejudice towards various attributes such as political affiliation, age, and religion. the italian dataset is an expansion of an italian hatespeech dataset that has been annotated for the existence of stereotypes towards muslims, roma, and immigrants. ## results and analysis hindi also performs well on english base model data due to a good overlap of religion, occupation, and race biases in both datasets. korean dataset has majorly gender bias instances along with other biases like political affiliation, religion, race, etc. only the english dataset has significant instances of gender bias in addition to korean. this improves the performance of the korean dataset when english is used as the base model. due to category overlap between the two datasets, such as political affiliation, religion, and race, the korean dataset also performs well (macro f1 of 74.5) when hindi is used as the base model. when measured using the xlm-r model, the average perplexity of the english, hindi, italian, and korean datasets are respectively, 77.05, 85.02, 103.23, 145.57. from perplexity scores and the performances of the monolingual model, it is evident that the korean dataset is complex in nature, and the gain in performance in the multilingual model for the korean dataset can be attributed to the learning from the source language (english or hindi). the italian dataset has a higher percentage of re-ligion biases than race biases, and both the italian and hindi datasets were gathered from their respective social media platforms. the english dataset was derived from a wiki corpus, whereas the korean dataset was derived from news articles. as a result, both hindi and italian dataset help each other. the results of multilingual few-shot experiments are shown in figure 3 and however, the correlation between the amount of target language fine-tuning data and the improvement in model performance is inversely proportional. the marginal gains of increasing 𝑁 decline sharply across models and target language test sets. for example, for hindi, xlm_l improves by 32 macro-f1 from 𝑁 = 25 to 400, and by just 4 from 𝑁 = 400 to 1600. similar trend is also observed for xlm_eng_l model. why translations can not be used directly? one fundamental question is whether we can utilise publicly accessible english datasets to forecast bias in datasets in other languages by translating them to english. ## conclusion and future work we present a comprehensive dataset of ∼ 9𝑘 hindi posts with multiple annotations: social bias and their categories, the sentiment of the post, the target group, and the rationales of the bias in the post. we demonstrate the capability of multilingual transfer learning using our dataset and publicly available english, italian, and korean datasets. multilingual fine-tuning (sequential fine-tune) is found to be effective for hindi, italian, and korean datasets, in the sense of reducing data requirements given a performance level or increasing performance level, given a fixed amount of data. our results show that irrespective of the language family (we have dealt with indo-european and the altaic family here), the bias detection task benefits from multilingual sequential fine-tuning. using few-shot experiments we show that only a small amount of target-language fine-tuning data is required to achieve strong performance and initial fine-tuning on english data can ameliorate data requirement. we report benchmarks on our dataset for the bias detection task in 4 languages. we plan to investigate the effect of multi-task training, for example, bias-and-sentiment, bias-and-explanation, and so on.