SlowGuess/NAACL_2022_OCR huggingface dataset:
As English MLMs, we use BERT $^{16}$ , multilingual BERT $^{17}$  (Devlin et al., 2019), RoBERTa $^{18}$  (Liu et al., 2019), ALBERT $^{19}$  (Lan et al., 2019), DistilBERT $^{20}$ , DistilRoBERTa $^{21}$  (Sanh et al., 2019), ConvBERT $^{22}$  (Jiang et al., 2020), XLM $^{23}$  (Connieu and Lample, 2019), and Deberta $^{24}$  (He et al., 2020). Since BERT and RoBERTa each use two models of different sizes, we use a total of 11 models. We report the averaged results over the above 11 models.
Table 3 shows that MBE has high performance in all evaluations. Performance of Shf highlights the importance of comparing male against female sentences in sentence pairs.
5.2 Gender Bias Evaluation Using Manually Annotated Data in Japanese and Russian
To validate MBE scores, which does not require evaluation data with identical context, nor manual creation of evaluation data in the target languages other than English, we use the following methods: HT: Native speakers manually translated all 262 sentence pairs in CP into Japanese and Russian and apply Equation 3. This human translated (HT) baseline can be seen as an upper bound for bias evaluation compared to MBE, which does not require translated examples. Lower difference from these bias scores in this human-translated (HT) method would indicate a more reliable bias evaluation measure. Note that, it is not appropriate to compare the bias score calculated using the English MLMs with the bias score calculated using the Japanese MLMs because we are evaluating dif
MLM	Bias score	Diff
HT(Japanese)	base-subword	52.67‡	-
large-subword	56.87‡	-
base-char	48.47‡	-
large-char	55.73‡	-
MT(Japanese)	base-subword	49.24	-3.43
large-subword	52.67‡	-4.20
base-char	54.20‡	5.73
large-char	45.80‡	9.93
MBE(Japanese)	base-subword	54.89‡	2.22
large-subword	55.85‡	-1.02
base-char	52.69‡	4.22
large-char	50.60	-5.13
Tmp(Japanese)	base-subword	88.31‡	35.64
large-subword	82.13‡	25.26
base-char	64.63‡	16.16
large-char	45.40‡	-10.33
ferent models. Therefore, we calculate the bias score in Equation 3 using the data translated into Japanese and Russian.
MBE: Here, we let MBE(Japanese) and MBE(Russian) be the MBE scores computed using the Equation 2 and parallel data created above by manually translating original (English) CP dataset into Japanese and Russian for Step 1, respectively.
MT: As an alternative to costly manual translations, we use Google Machine Translation method  $(\mathbf{MT})^{25}$  to translate sentence pairs in CP sharing identical contexts into each target language and apply Equation 3.
Tmp: Although it requires some knowledge about the target language, one can create templates in the target language for both genders such as "[Gender]は[Occupation]てす" ([Gender] is a/an [Occupation]) in Japanese, and fill in male and female word pairs, and occupation words as in "彼/彼女は医者てす" (He/She is a doctor) to create an equal number of sentences as the evaluation data for Equation 3. In the template-based method (Tmp), five word pairs were used for Japanese and Russian following prior work by Kurita et al. (2019)[26].
Table 4: The CP bias scores for manually translated CP to Japanese and bias scores for machine translated CP and the proposed method MBE. Diff shows the difference between MT, MBE and Tmp bias scores and HT bias scores, respectively.  $\ddagger$  indicates statistically significant difference at  $p < 0.05$ .
MLM	Bias score	Diff
HT(Russian)	wiki&nwes	46.95‡	-
subtitle&sns	48.85‡	-
MT(Russian)	wiki&nwes	49.62	2.67
subtitle&sns	50.38	1.53
MBE(Russian)	wiki&nwes	46.05‡	-0.90
subtitle&sns	48.82‡	-0.03
Tmp(Russian)	wiki&nwes	34.87‡	-12.1
subtitle&sns	63.51‡	14.7
Table 5: The CP bias scores for manually translated CP to Russian and bias scores for machine translated CP and the proposed method MBE. Diff shows the difference between MT, MBE and Tmp bias scores and HT bias scores, respectively.  $\ddagger$  indicates statistically significant difference at  $p < 0.05$ .
The templates were “[Gender]は[Occupation]て”。 and “[Gender]は[Occupation]に興味のは。” in Japanese and “[Gender] - [Occupation].” and “[Gender] - [Occupation] посяни.Jьности.” were used for Russian. We extracted respectively 644 and 154 occupation words for Japanese and Russian from Wikipedia27. Following prior work by Kurita et al. (2019), we generated respectively 6400 and 1500 template sentences for Japanese and Russian, and evaluated them using sentence pairs with identical contexts.
For Japanese MLMs, we evaluate four Japanese BERT models (base-subword $^{28}$ , large-subword $^{29}$ , base-char $^{30}$ , large-char $^{31}$ ), subword-based and character-based, with base and large sizes. For Russian, we use two MLMs – one trained on Wikipedia and news data (wiki&news) $^{32}$  and the other on OpenSubtitles (Lison and Tiedemann, 2016) and SNS data (Shavrina and Shapovalova, 2017). For Japanese and Russian, we use the difference of the bias scores instead of the correlation coefficients
sian: OH:OHa, MyJxuHa:KeHnHa, IaIa:Mama, BpaT:CeCTpa, IyIa:TeTg (English: He:She, Man:Woman, Father:Mather, Brother:Sister, Uncle:Aunt)
27https://ja.wikipedia.org/wiki/職業一覽 and https://ru.wikipedia.org/wiki/Ka te r o p n y:П r o p o f e c c n i
28https://huggingface.co/cl-tohoku/ bert-base-japanese-v2
29https://huggingface.co/cl-tohoku/ bert-large-japanese
30https://huggingface.co/cl-tohoku/ bert-base-japanese-char-v2
31https://huggingface.co/cl-tohoku/ bert-large-japanese-char
32https://huggingface.co/DeepPavlov/rubert-base-cased
25In July 2021, we translated CP data using google spreadsheet function: https://support.google.com/docs/answer/3093331?hl
26Japanese: 彼:彼女, 男:女, 父:母, 兄:姊, 叔父:叔母. Rus-
MLM	Bias score	Diff
\(HT_{name}\)(Japanese)	base-subword	52.29‡	-0.38
large-subword	54.58‡	-2.29
base-char	48.47‡	0.00
large-char	53.44‡	-2.29
\(HT_{name}\)(Russian)	wiki&news	47.33‡	0.38
subtitle&sns	48.09‡	-0.76
Table 6: The difference between the bias score for the original data and the bias score for the CP data translated into Japanese and Russian with the names of people replaced by Japanese and Russian, respectively.  $\ddagger$  indicates statistically significant difference at  $p < 0.05$ .
with HT because the number of publicly available pre-trained MLMs is smaller than that of English.
Tables 4 and 5 show the bias scores of HT, MT,Tmp and MBE and their differences measured against HT for Japanese and Russian MLMs, respectively. We see that the difference between the bias scores of HT and MBE are smaller than that for MT, indicating that MBE closely approximates the human bias ratings in HT than other alternatives. Moreover, we see that the direction of bias is reversed for base-char, large-char, and subtitle&sns compared to HT. Note that we can not directly compareTmp with other methods due to the difference in evaluation data. However, as one of the previous bias evaluation methods,Tmp overestimates the biases of MLMs, especially for Japanese subwords. This is because simple artificial templates often over-emphasize gender biases compared to natural sentences. Interestingly, MBE is more accurate than MT when evaluating gender biases. Further investigations revealed that MT model itself could produce gender-biased translations, thereby adding noise to the translated sentences.
6 Bias in Personal Names
One of the most significant differences in the frequency of words used in each language that affects gender bias is the names of people. In bias evaluation, male and female names are used to identify the gender (Caliskan et al., 2017; Romanov et al., 2019). However, when names are transliterated from English to the target language, those transliterated names might be infrequent in the target language and might not be gender representative. To study the effect of this issue on gender bias evaluation, we conduct the following experiment. First, for the Japanese and Russian target languages, we replace the transliterated English names in the CP
Figure 2: Percentage of manually translated sentences preserving gender information from English News data.
data with native Japanese and Russian names of the same gender. Next, we compare the bias scores with those before the replacement in Tables 4 and 5. We extracted the top 10 most popular names among Japanese $^{33}$  and Russians $^{34}$  for both genders, and randomly substituted them with the transliterated English names. For example, we rewrite "シエローはナーフです" → "美咲はナーフです" ("Shelly is a nurse" → "Misaki is a nurse").
Table 6 shows the MBE score for Japanese (HTname(Japanese)), and Russian (HTname(Russian)) after the name replacement and the corresponding differences w.r.t. original bias scores shown in Tables 4 and 5). We can see that the bias scores of the Japanese base models and all the Russian models are almost the same compared to respective values in Tables 4 and 5. The large models for Japanese differ by about -2.29, which is lower than the baseline in the table. Moreover, the direction of the bias has not changed in both languages compared to respective directions in Tables 4 and 5. These results suggest that the bias can be evaluated reasonably even when English names are transliterated into a target language.
7 Preserving Gender in Parallel Corpora
Step 1 of the proposed method requires that gender information in English (source) sentence matches that with the target translation in the parallel data. To test for this, we examine the proportion of sentences in which the corresponding translated words of English "she" and "he" appear to determine whether female or male gender information is retained. We use the News corpus and select Japanese
$^{33}$ https://www3.nhk.or.jp/news/special/sakusakukeizai/articles/20181127.html  
 $^{34}$ https://znachenie-tajna-imeni.ru/top-100-zhenskih-imen/ and  
https://znachenie-tajna-imeni.ru/top-100-muzhskih-imen/
and Russian, which had no bias, and German and Portuguese, which had significant biases (Table 2).
Figure 2 shows the percentage of sentences where gender was retained for male and female sentences in the target languages<sup>35</sup>. For German, Portuguese, and Russian, gender is retained in more than  $80\%$  of the sentences. This suggests that when the percentage of gender-preserved sentences is large, it does not affect the MBE score. In Japanese, gender information is retained in only about  $60\%$  of sentences, which is much lower than in other languages. This may be because Japanese is a null-subject language that allows independent clauses to omit explicit subjects. In fact, in some cases, gender words were omitted in the parallel corpus, for example “He owns a grocery store and runs a motorcycle rental business.” was translated to “自分の食料品店を持ち、レntonタルバイクビジnehスも営てる。（Owns a grocery store and runs a rental motorcycle business.)”。Contrarily, from the results in Table 4, MBE(Japanese) can detect the bias better than other methods. The reason may be that even if the gender words are omitted if the context is composed of words that often co-occur with male and female words, it is possible that it complements the gender information. In fact, Bolukbasi et al. (2016) show that words that co-occur with male and female words retain gender information. The results also show that gender preservation is not heavily biased in either the male or female direction, based on the small difference between percentages for male and female sentences for each language. This suggests that the bias in the preservation of gender information may not affect the evaluation of the proposed method.
In this paper, we showed that a bias evaluation data and evaluation of MLMs for discriminatory bias can be systematically created as long as there is a parallel corpus of English and the target language and a list of female and male words in English. Our meta-evaluation proved that the proposed multilingual bias evaluation method could perform correct evaluation comparing against method using manually created data, at least for Russian, Japanese, and English. The experimental results show that gender bias exists in all eight languages of our experiments. We also showed that the proposed method is supe
rior to the methods that use machine translation to translate the English bias evaluation data into the target language and the methods that use templates and word lists.
This paper is based on results obtained from a project, JPNP18002, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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