Gender Coreference and Bias Evaluation at WMT 2020 (2020)

Abstract: Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.

## introduction bias in machine learning occurs when systems pick up correlations which are useful for specific training datasets, but are not indicative for the task that the dataset represents. in the context of machine translation (mt), gender bias can occur when translating from languages without grammatical noun genders, such as english or turkish, to a language with gender inflections, such as spanish, polish, or czech. in such cases, the translation model needs to assign gender inflection in the target language based on contextual cues in the source text. for example, when translating the english sentence "the doctor asked the nurse to help her in the operation" to spanish, the model needs to produce the female inflected "doctora" based on the feminine english pronoun "her". recently, @xcite created a challenge set and an automatic evaluation metric, dubbed winomt, to examine whether popular mt models are capable of correctly capturing and translating such information from english into a diverse set of 8 target languages with grammatical gender. they found that all six tested systems, composed of four commercial and two academic models, consistently relied on gender role assignments in the data regardless of context. in our example above, models would prefer to translate the doctor using masculine inflections, despite the context suggesting otherwise. in this work, we apply the winomt test suite on the submissions to the news shared task of wmt 2020. in addition to testing the phenomenon on a large number of models, we extend the winomt to the polish and czech languages, tackling unique language-specific challenges. we thoroughly analyze the extent of the phenomena for the tested languages and systems, as well as its correlation with the widely-used bleu evaluation metric @xcite , finding that systems with worse performance (in bleu) make more errors for female professions than errors for male professions. on the other hand, better-performing systems (in bleu) make more errors related to anti-stereotypical professions (e.g. female doctors, or male nurses). similarly to the conclusions of @xcite , we find that all systems consistently perform better when the source texts exhibit stereotypical gender role assignments (e.g., male doctors, female nurses) versus non-stereotypical assignments (e.g., female doctors, male nurses), indicating that these models rely on spurious correlations in their training data, rather than on more meaningful textual context. we hope that this evaluation will be used as a standard evaluation metric for mt as a means to track the improvement of this socially important aspect of translation. ## background: winomt winomt was created as a concatenation of two coreference test suites: winogender @xcite and winobias @xcite . each instance in these datasets is a single english sentence, presenting two entities, identified by their profession (e.g., "teacher", "janitor", or "hairdresser") and a single pronoun referring to one of them based on the context of the sentence. for example, in the sentence "the physician hired the secretary because he was overwhelmed with clients", the marked pronoun refers to the physician. in contrast, in "the physician hired the secretary because he had good credentials" the pronoun likely refers to the secretary. both datasets are created with an equal amount of stereotypical gender role assignments (e.g., the first example) and non-stereotypical assignments (e.g., the second example). both works found that coreference systems performed much better on the stereotypical role assignments than they did on the non-stereotypical ones, concluding that systems relied on training correlations between pronoun gender and professions rather than the syntactic and semantic information in the input sentence. @xcite use these two corpora to test gender bias in machine translation in the following manner: following this procedure, they tested four commercial systems and two state-of-the-art academic models on eight diverse target languages: spanish, french, italian, hebrew, arabic, ukrainian, russian, and german. in all of their experiments, they found that similarly to coreference models, mt systems are prone to make gender-biased predictions. in section 3, we describe our extension of winomt to two additional languages tested in wmt 2020: czech and polish, and in section 4 we use the extended test suite to evaluate wmt sub-mission on a total of four target languages: czech, german, polish, and russian. in this section, we describe our extension of winomt for czech and polish. the methods and analyses for both target languages are done by the first two authors, who are native speakers in the respective language. for both languages, we followed the approach of winomt, where translated sentences are first aligned by fast_align @xcite , followed by automatic morphology analysis. besides, we notice that the automatic alignment and existing tools sometimes fail leading to "unknown" gender decision. for both czech and polish, it could not recognise on average 10-15% test examples. fortunately, both languages have rich morphology where gender can be often identified from the word form. therefore, we have created a list of the most often translations of each profession in all cases. example of such a list is in this step significantly reduced the number of unrecognised genders. in section 3.4, we discuss the number of unrecognised genders of the profession. ## evaluation we continue with the analysis as described by @xcite . for each system, we compute three metrics that represent their ability to resolve gender coreference; or how often the systems resolve the gender-based on stereotypical genders of professions. all results are in ## conclusions we have extended analysis by @xcite with czech and polish languages. we showed that current systems, both commercial and academic, perform worse in gender coreference when profession in question is female. this is amplified if the system in question has lower translation quality. moreover, systems rely for translations on stereotypical genders of professions instead of correct gender coreference resolution. we showed that with increasing translation quality, models make fewer errors in general, but rely more often on the stereotypical genders.