Evaluating Bias In D utch Word Embeddings (2020)

Abstract: Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world applications. This paper explores the gender bias implicit in Dutch embeddings while investigating whether English language based approaches can also be used in Dutch. We implement the Word Embeddings Association Test (WEAT), Clustering and Sentence Embeddings Association Test (SEAT) methods to quantify the gender bias in Dutch word embeddings, then we proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods and finally we evaluate the performance of the debiased embeddings in downstream tasks. The results suggest that, among others, gender bias is present in traditional and contextualized Dutch word embeddings. We highlight how techniques used to measure and reduce bias created for English can be used in Dutch embeddings by adequately translating the data and taking into account the unique characteristics of the language. Furthermore, we analyze the effect of the debiasing techniques on downstream tasks which show a negligible impact on traditional embeddings and a 2% decrease in performance in contextualized embeddings. Finally, we release the translated Dutch datasets to the public along with the traditional embeddings with mitigated bias.

## introduction in recent years language models have become more relevant in the field of natural language processing (nlp). as word embeddings were shown to perform better in many tasks than many traditional techniques, the research community followed this direction and made further advancements resulting in another breakthrough -contextualized word embeddings (e.g. @xcite , roberta @xcite ). as opposed to traditional (context-free) word embeddings that have a fixed representation vector, contextualized word embeddings change depending on the sentences (context) in which they are used. they have achieved state-of-the-art in nlp tasks and effectively replaced traditional word embeddings. due to their outstanding performance, they are broadly adopted in many real-world applications @xcite . models employing those embeddings often support decisions that strongly impact people's lives, so their fairness and correctness is critical. unfortunately, previous research has shown that traditional and contextualized word embeddings can encode social biases present in the training data @xcite @xcite . the social biases in machine learning applications can have an impact on society, as the case in computer vision where three commercial gender classification systems reported higher error rates when recognizing women, specifically those with darker skin tones @xcite . these biases can cause undesired effects in downstream nlp tasks @xcite where biased nlp models can amplify bias in real world applications and especially affect minorities which are misrepresented in the data. it has been shown that some minorities like people with disabilities are misrepresented and are associated with a negative sentiment @xcite . this paper explores the existing gender bias in six traditional and two contextualized dutch word embedding models by using a combination of state-of-the-art methods proposed in previous literature for english @xcite . then, we attempt to mitigate the gender bias in these models and analyze the effect of this step in an applicable downstream task. @xcite ) therefore we answer the following questions: ## related work in social psychology the implicit association test (iat) @xcite is used to measure the strength of implicit associations between concepts (e.g., black people, gay people) and evaluations (e.g., good, bad) or stereotypes (e.g., athletic, clumsy). when doing an iat a participant is asked to quickly sort words into categories that are on the left and right hand side of the computer screen. the iat has five main parts and the score is based on how long it takes a person, on average, to sort the words in the third part of the iat versus the fifth part of the iat. we would say that one has an implicit preference for thin people relative to fat people if they are faster to categorize words when thin people and good share a response key and fat people and bad share a response key, relative to the reverse. similarly to the iat, research in traditional embeddings has proposed how to identify gender bias by measuring the distance between gendered words like pronouns and neutral nouns @xcite , while there has been some relevant work done towards mitigating the bias in the embeddings @xcite , it has been demonstrated that some of the methods are not enough and the bias can remain hidden within the embeddings. regardless of the distances in the gender dimension, frequency of words are not taken into account @xcite and biased words can remain clustered together @xcite . furthermore, these bias methods have to be modified when used on contextualized word embeddings because the embedding representation of every word changes depending on the context in the sentence. a modified method is sent-debias @xcite which relies heavily on the sentences used to reduce the bias and we hypothesise that due to the extensive use of the pronouns he and she in english, which are not used in dutch due to their multiple meaning, the mitigation step encompasses a smaller gender subspace in comparison to english and thus the bias is reduced less. a predisposition towards english word embeddings exists when researching bias, where the proposed mitigation techniques cannot be directly applied in non-genderless languages. moreover, there is a lack of work in debiasing contextualized embeddings for other languages that contains characteristics that prohibits to simply import the original methods, like dutch with zij meaning both she and they in english. while some work has been done in languages that contain grammatical gender like spanish and french @xcite , these proposed methods are restricted to traditional embeddings. this paper looks into the intersection of the aforementioned techniques by analyzing and mitigating gender bias in traditional word embeddings and the state-of-the-art dutch models bertje @xcite , robbert @xcite while providing a version of the weat and seat data for this task in dutch @xcite . ## bias statement examples of english nlp models bringing negative consequences to misrepresented groups like an algorithm penalising job applications that contained words like "woman's" and "women's chess club captain." @xcite , or high paying jobs advertisements being shown less to woman than to man @xcite are real world cases which could be repeated in dutch speaking countries, if these dutch models are deployed without considering the bias present in the word embeddings. in this paper we study the bias by means of embeddings cosine similarities showing that after applying a debiasing technique, other different forms of bias like clustering can remain by having gender stereotype words grouped together enforcing their association. a nlp recommendation system associating words as caring and artistic to girls while using mathematician and sportive to boys could enforce gendered roles and a gender unequal society when for example, recommending toys or books as it has been studied in psychology @xcite . the high usability of language models has allowed a broad adoption of these techniques in real world applications, by mitigating different types of bias in nlp models we could not only avoid amplifying these biases but shift the social balance in the long term by avoiding algorithms enforcing social biases against minorities. ## methods and data in the following section we explain the methods and data used in this research. multiple sets of data (e.g. pairs of sentences, lists of gendered words and combinations of sentences of different categories), are needed as a means to analyze and mitigate bias in word embeddings, three algorithms are implemented to measure bias in embeddings; two being applicable to traditional embeddings and the third one is a method adapted for contextualized word embeddings. then we show the approaches we use to mitigate the bias in either type of embeddings and test the performance of the bias mitigation on downstream tasks. furthermore we recall this research is done on dutch embeddings, thus the data used is from the same language translated from the english data used on the methods original research. ## experiments and results in this section we explain how we use the methods and data explained in previous sections to measure and mitigate the bias in dutch embeddings. we demonstrate the existence of bias in traditional word embeddings and explain the bias mitigation process that has been used. then we demonstrate that contextualized word embeddings show less bias than traditional ones while also determining that the bias mitigation is not as effective in contextualized word embeddings as in traditional word embeddings. ## discussion in the traditional word embeddings we see large effect sizes (@xmath0.0) in every weat test that passes the significance statistic and we manage to decrease these scores with the hard-debias method. we also note that the bias mitigation on tests that uses names in either english or dutch doesn't perform as well as in the other tests that terms of groups are used. we hypothesize that while names can encode bias, it could be in a different subspace than the group terms and if not enough names are used in the data of hard-debias to generate the gender subspace then the mitigation fails when dealing with names. we point out that hard-debias has an equalizing step that depends in a list of gendered word pairs which should not be transformed by the bias direction and instead centralized where each pair is separated by the same distance. this can be a limitation on the method since it heavily relies in this list of gendered 13 confidence intervals are calculated using the normal approximation to the binomial distribution. the clustering metric shows that even if the bias is reduced using hard-debias and tested on weat, there can be other approaches to identify bias and some methods won't mitigate the bias from that point of view, therefore we suggest more research should be done in different ways to identify bias and mitigate it. the results on the relation identification task showed that the bias mitigation step had a slight influence on the model results, but overall the method shows that hard-debias does not affect negatively in a remarkable amount the performance on the task. as for the results of bertje and robbert, they both show bias in the same tests associated to gender but present a smaller effect size overall (@xmath1.0) compared to traditional embeddings on weat. when using sent-debias the effect sizes decreases along the significance statistic of the bias, yet the tests remain in the same statistical significance category (@xmath2.05 ∨ @xmath3.01). this shows that the bias mitigation technique works but it does not completely eliminate the bias. a possible reason to this result is that contextualized words embeddings contain a more complex gender subspace and a stronger method might be necessary in order to mitigate the bias on it. moreover, we show on the sentiment analysis task that similarly to the english research @xcite , when mitigating the bias the accuracy on downstream tasks decreases by a small percentage, this demonstrates that methods built for english embeddings can also be used for dutch ones and produce similar results on equivalent tasks. one of our work's limitations is that while our methods are directly applied in dutch, our test data was created in english and adapted for dutch. that means that our tests do not specifically take into consideration some of the linguistic features in dutch that are not present in english, such as the use of diminitives (e.g. @xmath4, regional languages (e.g. limburgish) or the afrikaans sister language. we also highlight that both weat and seat don't test for the lack of bias, just test whether if in the test cases bias exist but there could be other non-tested cases where bias is present. an example of this is shown when computing the clustering accuracy test which demonstrates that if we measure bias from a different point of view, the bias remains and thus further research in different bias mitigation techniques is recommended. ## conclusion with this research we show that among others, gender bias is present in dutch traditional and contextualized word embeddings (as probably in other languages as well). we then show how methods used to measure and mitigate bias in english embeddings, can be used in dutch embeddings by properly translating the data and taking into consideration the unique characteristics of the language (e.g. zij/ze). furthermore, we analyze the effect of the mitigating techniques in downstream tasks showing negligible impact in traditional embeddings and approximately a 2% decrease of performance in contextualized embeddings, which can be considered detrimental if the embeddings used guarantee a more gender-neutral approach. a promising future direction stemming from this research would be the development of extensive bias tests taking into consideration dutch dialects and other language peculiarities. moreover, more research is needed into the evaluation of different definitions of bias in contextual embeddings as we have shown in the findings that clustering bias persists regardless of the weat results in traditional embeddings. with this paper, we highlight the existence of bias in commonly used representation models, therefore, we advocate the use of mitigated bias models, especially in many industry applications (e.g. real-world nlp models) where bias existence can harm minorities. finally, we release the new dutch datasets for all bias tests to the public foot_12 and the traditional embeddings with mitigated bias.