The (Undesired) Attenuation of Human Biases by Multilinguality (2022)

Abstract: Some human preferences are universal. The odor of vanilla is perceived as pleasant all around the world. We expect neural models trained on human texts to exhibit these kind of preferences, i.e. biases, but we show that this is not always the case. We explore 16 static and contextual embedding models in 9 languages and, when possible, compare them under similar training conditions. We introduce and release CA-WEAT, multilingual cultural aware tests to quantify biases, and compare them to previous English-centric tests. Our experiments confirm that monolingual static embeddings do exhibit human biases, but values differ across languages, being far from universal. Biases are less evident in contextual models, to the point that the original human association might be reversed. Multilinguality proves to be another variable that attenuates and even reverses the effect of the bias, specially in contextual multilingual models. In order to explain this variance among models and languages, we examine the effect of asymmetries in the training corpus, departures from isomorphism in multilingual embedding spaces and discrepancies in the testing measures between languages.

## introduction the perception of odor pleasantness has been shown to be universal. even if variations across individuals exist, they do not depend on culture @xcite . we call this is a human bias: judging a phenomenon in terms of values that are inherent in human beings, regardless of their sociocultural background. on the contrary, a cultural bias is "the tendency to interpret and judge phenomena in terms of the distinctive values, beliefs, and other characteristics of the society or community to which one belongs". 1 racism, sexism, ageism, etc. are all social cultural biases and can be more or less present in distinct communities. as long as neural systems are trained on generaldomain texts written by humans, one would expect and desire human biases to be present in embedding models for all languages. one would also expect (but not always desire) cultural biases, but these would depend on the language or the culture behind it. lots of work has been done on detecting social cultural biases and trying to mitigate them @xcite @xcite @xcite @xcite . also recent work has investigated the presence of human biases in static word embeddings; first in english @xcite and later in other languages @xcite . these works make use of the word embedding association test (weat) for english -lists of concepts and attributes that hide implicit human associations as described in section 2-and x-weat, weat's translations, for other languages. to the best of our knowledge, research on language models and contextualised embeddings, has been done only in english, using extensions or variations of weat @xcite @xcite . natural language processing aspires to multilinguality. when talking about embeddings, one achieves multilinguality by mapping two or more spaces into a single one, or by joint training with text in two or more languages. these are the two main approaches to crosslingual word embeddings @xcite and the approaches used to train language models @xcite @xcite . we go deeper into the understanding of multilingual embedding models and the effect, if any, of the different approaches used to build them. for this purpose, we first review results and some of the implicit assumptions made in previous works by investigating (i) whether translations from the original weat english lists (x-weat) are fair tests for other languages and (ii) whether a single list, either original or translated, is representative for a language. second, for the first time we define and collect cultural aware weat lists (ca-weat) to enable multilingual analyses completely independent of english. third, we use weat, x-weat and ca-weat to study the effect of crossand multilinguality in embedding models; that is, how they differ with respect to the monolingual english. since differences do exist, we try to explain them in terms of the testing measure (*-weat), the nature of the training corpora, the method to achieve multilinguality and the final differences in the topology of the embedding spaces between languages. we perform a systematic study over 9 languages of 3 families in word embeddings from 16 static and contextual models. our premise is that biases should be equally present, and we analyse departures from this premise with the focus on multilinguality. the word embedding association test (weat) @xcite is a bias measurement method for word embeddings. weat is inspired by the implicit association test (iat) for humans @xcite , which measures differences in response time when subjects are requested to pair items and attributes that they find similar and when pairing items and attributes that they find different. to give an example, subjects would be first asked to label the item orchid as flower-pleasant or insect-unpleasant. this would be repeated for several flowers and insects. in a second part, subjects would be asked to label the same list of items as insect-pleasant or flower-unpleasant. experiments show that the response time for the first part is lower than for the second one. the cognitive effort for the latter is higher because the association flower-unpleasant is less expected than flower-pleasant. these results expose a human bias: flowers are more pleasant than insects and insects are more unpleasant than flowers. if this is a universal human bias, one would expect it to be present also in embeddings created from human texts. flowers in a semantic space should be closer to pleasant attributes than insects, and insects closer to unpleasant attributes than flowers. many weat (and iat) tests exist; most of them are designed to measure biases (implicit associations) towards racial groups, gender, sexuality, age, and religion. only two are nonsocial and therefore we expect them to be cultureand language-independent: flowers/insects vs. pleasant/unpleasant (weat1) and musical instruments/weapons vs. pleasant/unpleasant (weat2). these are considered "universally accepted stereotypes" @xcite . each attribute and concept in these tests has associated a list of 25 english terms, collected as described in section 3 (listed in appendix c). the original weat measure @xcite defines the association of each term t (e.g. orchid) as its average cosine similarity to the list of target attributes a (e.g. pleasant concepts): we use cohen's d to estimate the effect size (i.e. the strength of the bias). cohen's d is a standardised measure of the effect defined as the difference between the two means divided by the standard deviation for all instances in x and y : ## multilingual aspects and ca-weat both iat and weat have been traditionally created in the north east of the us and performed in english. the pleasant and unpleasant words used in weat1 and weat2 were selected from norms in @xcite , where college students in ohio rated a list of words for pleasantness. from this list, 25 elements were taken as pleasant words and 25 as unpleasant words by @xcite . the lists for flowers, insects, musical instruments, and weapons were extracted from @xcite , where college students from maryland and illinois were given 30 seconds to write down as many objects within each category as possible. @xcite selected 25 unambiguous items that they thought their students would be familiar with. these two requests are relevant: they ensure taking frequent words in the language that have a single meaning. the first experiment with word embeddings was done by @xcite who used pretrained english glove embeddings @xcite . they observed that, according to their results on social biases, the training corpus "may be disproportionately american". subsequent studies used crosslingual weat (x-weat) to go beyond analyses in english @xcite . the original lists were translated into several languages and biases estimated using the translated items and attributes. they found differences in the biases obtained across languages and connected them to differences in the size of the training corpora. they also explored bilingual spaces and observed that the bias effects were in the middle of the two corresponding biases in the monolingual spaces. as discussed, weat1 and weat2 originate in the us. even if a concept (flower) might be considered pleasant in every culture, the items themselves (orchid, broom, etc.) can be different across cultures. this is most evident for elements that depend on geography (a flower that grows in the us or an insect that lives there might not be present in other locations), but it could happen for all the other items in weat1 and weat2. as a result, the representation of the original translated items in the training data in other languages might be smaller or, even worse, the distribution of the opposite attributes asymmetric. as we will show, there is already a variation in the terms and attributes used by different people within a common culture, but testing the existence of human biases with translations from american english might be inducing an additional cultural bias in the results. there is a second argument to avoid x-weat: translations are not perfect and one cannot assure that the requirements in greenwald et al. (1998) (unambiguous and frequent words) hold. for example, the spanish x-weat translates blade as hoja (the edge of a knife, but also a sheet of paper) and turns both fiddle and violin into violín. whereas correct, translation introduces an ambiguous word lacking any association to (un)pleasant attributes in the former case, and reduces the size of the list in the latter. to mitigate the problems introduced by translations and to estimate their impact in the analysis, we create ca-weat: a new collection of culturalaware lists written by native speakers of different languages. we asked volunteers to create lists of flowers, insects, weapons and musical instruments, as well as both pleasant and unpleasant concepts with 25 elements each without any time constraint. the only requirement was that words needed to be common in their culture. lists from different volunteers are semantically equivalent, since they characterise the same concepts, and can be seen as perturbations on a prototypical (or average) set. we first conducted a pilot study with 14 volunteers from 11 nationalities to survey the difficulty of the task and prepare the guidelines of the experiment. five of them failed to complete the task. after the pilot, we set up an online form with detailed instructions (see appendix a) and distributed it to contacts in different countries. we collected 112 ca-weat lists in 26 languages from which we discarded 9 after a quality check. 3 for the current experiments, we selected 9 of the 26 languages, for a total of 82 lists. the lists in the remaining 17 languages are provided in the ca-weat dataset but we do not use them in the subsequent analysis; statistics for all of them are reported in appendix b. the languages considered here are chosen according to 3 criteria: highquality embeddings could be obtained, the equivalent x-weat exists or could be created by a native speaker at hand, and different language families are covered. these constraints led to considering arabic (ar), catalan (ca), croatian (hr), english (en), german (de), italian (it), russian (ru), spanish (es) and turkish (tr). the distribution among languages is not even: we collected 24 lists in italian and german, 12 in croatian, 10 in spanish, 5 in english, 2 in catalan, romanian and turkish, and 1 in arabic. instead of aggregating the highest ranked/most frequent words per concept into a single list as weat does, ca-weat uses all the words and provides a list per subject. this allows us to study statistically the variations and the relevance of the test sets. for x-weat, we use the translations provided by @xcite and @xcite , after revising the spanish ones and adding the catalan translations. ## embedding models we consider 16 embedding models for the 9 selected languages. we select two kinds of models. on the left-hand side, widely-used out-of-the-box pre-trained models. on the right-hand side, models trained in-house. for the latter, we control both the amount and domain of the training data, as well as the approach used to reach multilinguality. for static embeddings, we use pre-trained fast-text word embeddings: foot_3 wp: monolingual models trained on wikipedia using the skip-gram architecture with subword information, as described by @xcite . wpali: wp aligned to english with the rcsls method as described by @xcite . ccwp: models trained on common crawl and wikipedia using cbow with position weights and subword information @xcite . we also build 5 static in-house word embeddings on common crawl using a subset of the cc-100 corpus @xcite . we enforce to have the same number of words for all 9 languages under study (cce) by ceiling the size to that of the language with the smallest corpus: catalan, with 1.7•10 9 words (see appendix d). for comparison purposes, the training of the 5 models is done with the same architecture and hyperparameters as in ccwp: cbow with position-weights, 300 dimensions, character 5-grams, a window of size 5 and 10 negatives. the five in-house embeddings are: cce monolingual embeddings trained on cce. ccevmuns cce aligned to the english space using unsupervised vecmap @xcite . 5 ccevmsup supervised vecmap @xcite using the test part of the cross-lingual dictionaries in muse. 6 cce2langs bilingual embeddings trained on the concatenation of cce-en and cce-l i for one of the other 8 l i languages. cce9langs multilingual embeddings trained on the concatenation of the 9 cce-l i . purely static embeddings are compared to word embeddings extracted from 3 pre-trained contextual models: @xcite ; trained on 104 languages including the ones we analyse with a bpe vocabulary of 110k. mbert 11 embeddings in the next-to-last layer (layer 11) of multilingual bert. foot_6 bert 0 we use monolingual bert for arabic @xcite , german, foot_7 italian @xcite , spanish @xcite , turkish @xcite and english @xcite . for the other languages, the model is not available or it finetunes mbert. bert 11 embeddings in the 11th layer of the same models as in bert 0 . xlm-r 0 static embeddings in xlm-roberta @xcite ; trained on 100 languages with a bpe vocabulary of 250k. xlm-r 11 embeddings in the next-to-last layer of xlm-roberta. xglm 0 static embeddings in xglm @xcite ; trained on 30 languages with a bpe vocabulary of 250k, excluding croatian. xglm 47 embeddings in the next-to-last layer (layer 47 in this case) of xglm. while static word2vec-like embeddings have 300 dimensions, bert embeddings have 768, xlm-roberta 1024 and xglm 2048. ## experiments and results we calculate the statistic and the effect size (cohen's d) for the 16 types of embeddings in the 9 languages for weat1, weat2, the 82 ca-weat1 and ca-weat2 and the 8 translated x-weat1 and x-weat2 lists. 9 for the statistical analysis of the results, we estimate the uncertainty on the statistic and the effect size by (i) averaging the results with multiple lists for ca-weat and (ii) creating bootstrapped versions of a single instance in all cases: weat, x-weat and ca-weat. for the average version, we provide the median and 95% confidence intervals (ci) using order statistics given that the distributions are non-normal and contain few elements. for the bootstrapped version, we resample with replacement the four lists involved in an experiment to generate 5,000 synthetic sets per test. 10 tables 6, 7, 8 and 9 in appendix e show the detailed results. 9 both the source code and the data to reproduce our analysis, including the revised x-and the ca-weat lists, are available at https://github.com/cristinae/ ca-weat . weat vs x-weat vs ca-weat. first, we compare the conclusions one gets using each of the three testing alternatives. figure 1 depicts the effect size estimations for x-weat and the median of the ca-weat tests. biases measured with x-weat are in general higher than those with ca-weat, specially for pure static embeddings, but differences are not statistically significant at 95% level. the dispersion within a model and across languages is smaller for ca-weat than for x-weat; an indication that a single list is not representative for a language and the average of several lists helps to get closer to a universal effect size value. the variation across lists of the same language is big. the top-row plots in figure 2 compare the effect size of the original weat1 test and 5 ca-weat1 lists created by native speakers of american english, taking 3 models as representatives. the bottom part of each plot shows the effect size for the ca-weat1 tests (grey and red) and the weat1 test (blue). the top part shows the histogram of the ca-weat1 tests and reports the numbers displayed in figure 1 . the five lists show very different behaviours across models, but also within a single model. for the monolingual model cce, we see that for three of the lists the effect is compatible with no bias at 95% level. one would expect the average of the english ca-weat lists to be close to weat, as the latter was obtained by combining inputs from several subjects. however, the variation is huge and depends on the model. in order to be able to substitute weat with ca-weat, one needs multiple samples. x-weat can be considered as just one of these samples and this can explain why the variation across languages is larger with x-weat than with ca-weat. the remaining plots in figure 2 compare x-and ca-weat1 by looking at the variation in more samples, the italian lists. the variation among ca-weats is large for all models, and x-weat results are within the cis of ca-weat. according to @xcite 's scale, the magnitude of the biases ranges from medium (@xmath0.5) to very large (<1.2) for x-weat and from small (<0.2) to large (<0.8) for ca-weat. similar trends are seen for german, also with 24 ca-weats. comparable experiments with cce. we compare the effect size d across languages and multilingual methods in the setting where all the embedding models are trained in a similar domain, using the same amount of data. the size of the corpus is not the only deciding factor though, as the number of times that the items in the lists appear in the corpus might also have an effect. words in the lists are frequent enough (billions of occurrences) to get high quality embeddings, but an asymmetry between the number of positive and negative concepts might create an artificial bias. we test this hypothesis by studying the correlation between the bias effect size and the difference between the counts of the positive (pleasant) and the negative (unpleasant) attributes. 11 figure 3 shows the relation for the monolingual cce embedding models. the correlation seems important for x-weat1 (top plot), which shows a positive trend with half of the variation in the effect size being explained by the number of counts r 2 =0.493. however, this might be an effect of either having only 9 data points or x-weat and ca-weat coming from two different distributions. when we consider the 82 ca-weat1 tests and the 9 x-weat1 (bottom plot), we observe a flat slope where the variance is not explained by the counts (r 2 =0.001). results for x-/ca-weat2 are equivalent, with r 2 =0.334 and r 2 =0.008 respectively. ca-weat lists allow to see that the lack of trend is language independent. the average effect size for spanish is higher than for german, the count difference is larger for english than for croatian, but in none of the cases can the asymmetry counts explain the effect size variance. multilinguality changes the distribution of effect sizes and also the attribute counts in the training corpus of some models. while ccevmuns and ccevmsup project the pre-trained spaces into a common one, cce2langs and cce9langs train the joint embeddings on the concatenated corpus. in this case, the counts change as different languages can share the same surface token for some words. this might be a reason why differences in biases with respect to english in languages with different scripts are more relevant than differences with languages in different families. but it is not the only reason, as we observe the same trend in the projection methods. in general, the 4 multilingual approaches share the same conclusions as their monolingual counterparts: for x-weat one observes a positive correlation between d and the difference of counts but, when inspecting all the ca-weat lists, the correlation disappears. cce9langs with the widest differences of counts shows almost no correlation for x-weat as well. multilingual models and isomorphism. going from monolingual cce to bilingual ccevmuns implies a mitigation of the bias for ca-weat but biases remain close to constant for x-weat (see figure 1 ). contrary to the findings by @xcite , the bilingual embeddings do not show a bias halfway between that of the 2 languages. the effect of supervision (ccevmuns vs ccevmsup) is not consistent and results are in general equivalent. with few exceptions, the effect of cce2langs and especially of cce9langs is also a mitigation of the bias with respect to the one observed in the corresponding monolingual model cce, but this is not statistically significant at 95% level. arabic and russian, both with non-latin alphabets, have the lowest d (together at times with turkish, the only other non-indo-european language), but we cannot attribute it to multilinguality, because their effect size is also low for the monolingual embeddings. when we move to the multilingual setting in our experiments, we do not consider sentences, but single words for which we extract the corresponding embedding. words are built from the subunits in the language model vocabulary but, other than that, no context is considered. this allows a fair comparison across languages. as figure 1 shows, static embeddings in contextual models show almost no bias and even negative effect sizes. the trend is specially confirmed for the languages for which more ca-weat lists are available (it, de, hr, es). also notice that multilinguality further blurs the biases. in general, monolingual bert models present less (desired) biases than wp, wpali and ccwp for all languages but more biases than the multilingual language models. this is not a consequence of a lower isomorphism between language subspaces, as ## summary and conclusions non-social human biases in embeddings are usually measured through weat association tests built in english, in the us. we hypothesise that this can be an issue when analysing embeddings in other languages or cultures. in order to address the question, we collected weat1 and weat2 lists written by natives of 9 languages, which we call cultural aware tests: ca-weat. we showed that different ca-weats produce a large variation in the biases and their effect size d. the values for weat and x-weat always lie within the ca-weat confidence intervals. this supports the idea that a single list (test) is not suitable for the analysis. since we do not have a gold standard for the real bias we should expect in embeddings, one could argue that the correct bias is the one given by the weat test, as it has been carefully designed. however, this argument only holds for (american) english. for any other language, x-weat cannot assure the same properties as weat. ca-weat, the multilingual crowdsourced versions of weat, are the alternative to x-weat we extend previous work to multilingual and language models taking this observation into account and perform in parallel the analysis for weat, x-weat and ca-weat. we confirm that monolingual static embeddings show signs of non-social human biases in all languages under study. when using the average ca-weat, the dispersion of d among languages for each model is smaller than the dispersion with x-weat. this is an indication that the average of several lists helps to get closer to the expected universal results across languages. multilinguality has the effect of mitigating biases. this is seen in static word embeddings but it is more evident in embeddings extracted from language models. on the other hand, word embeddings in language models already produce a huge mitigation with respect to their static counterparts, up to the point that effect sizes in multilingual language models can be negative. as a result, the trend can be inverse to the one observed in humans, being insects more pleasant than flowers in some languages. unexpectedly, the asymmetry between the amount of pleasant and unpleasant attributes in the training corpus is not responsible for the variance in the embeddings biases. since ca-weat includes only frequent words in our training corpora, reliable representations are obtained, irrespective of any asymmetry. differences in departures from isomorphism between languages in multilingual models describe up to a 10% of the variance. even the trend is clear, this alone cannot explain the mitigation of the biases in either multilingual or contextualised models. in the light of these outcomes, we expect to broaden the analysis to a more diverse set of languages extending the ca-weat tests, and design a fair multilingual setting for language models at sentence level.