On the Dynamics of Gender Learning in Speech Translation (2022)

Abstract: Due to the complexity of bias and the opaque nature of current neural approaches, there is a rising interest in auditing language technologies. In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST). As a new perspective, rather than focusing on the final systems only, we examine their evolution over the course of training. In this way, we are able to account for different variables related to the learning dynamics of gender translation, and investigate when and how gender divides emerge in ST. Accordingly, for three language pairs (en ? es, fr, it) we compare how ST systems behave for masculine and feminine translation at several levels of granularity. We find that masculine and feminine curves are dissimilar, with the feminine one being characterized by more erratic behaviour and late improvements over the course of training. Also, depending on the considered phenomena, their learning trends can be either antiphase or parallel. Overall, we show how such a progressive analysis can inform on the reliability and time-wise acquisition of gender, which is concealed by static evaluations and standard metrics.

## introduction along with the massive deployment of language technologies, concerns regarding their societal impact have been raised @xcite , and glaring evidence of biased behavior has been reported by users themselves. translation technologies are no exception. on-line interactions exhibited that commercial engines reflect controversial gender roles @xcite , and further evaluations on both machine (mt) and speech translation (st) systems confirmed that models skew towards a masculine default @xcite @xcite , except for stereotypical representations (e.g., nurse or pretty doctor as feminine) @xcite . the last few years have witnessed a growing effort towards developing preventive measures @xcite and mitigating strategies @xcite @xcite ). yet, the complex nature of both neural approaches and bias calls for focused inquiries into our st and mt models. in this regard, dedicated testing procedures have been designed to pinpoint the impact of gender bias on different categories of phenomena @xcite @xcite . also, algorithmic choices underpinning the construction of current models have been reevaluated in light of gender disparities @xcite . despite such promising advancements, many questions still stand unanswered. when does this gender gap emerge? how does gender bias relate to progress in terms of generic performance? to what extent is gender learning altered by the chosen components? to the best of our knowledge, current studies have adopted a static approach, which exclusively focuses on systems' biased behaviors once their training is completed. rather than treating training as a black box, in this paper we explore the evolution of gender (in)capabilities across systems' training process. in the wake of prior work highlighting how different target segmentations affect gender bias @xcite , we compare st systems built with two techniques: character and byte-pair encoding (bpe) @xcite . for three language pairs (@xmath0,fr,it), we thus examine their gender learning curves for feminine and masculine translation at several levels of granularity. overall, our contributions can be summarized as follows: (1) we conduct the first study that explores the dynamic emergence of gender bias in translation technologies; also for automatic translation, inspecting models' inner workings @xcite can help disclosing potential issues or explaining viable ways to alleviate the problem @xcite . concurrently, studies in both mt and st foregrounded how taken-for-granted algorithmic choices such as speed-optimization practices @xcite , byte-pair encoding @xcite , or greedy decoding @xcite ) -although they may grant higher efficiency and performance -are actually disfavoring when it comes to gender bias. finally, fine-grained analyses based on dedicated benchmarks have shown the limits of generic procedures and metrics to detect gender disparities @xcite . such contributions are fundamental to shed light on gender bias, by providing guidance for interventions on data, procedures and algorithms. in this work, we contribute to this line of research by analysing direct st systems @xcite . as an emerging technology @xcite , we believe that prompt investigations have the potential to inform its future development, rather than keeping concerns over gender bias as an afterthought. in the wake of previous studies pointing out that i) st systems may exploit audio cues to translate gender @xcite , and ii) state-of-theart bpe segmentation comes with a higher gender bias @xcite , we conduct fine grained analyses on these systems, but by means of a new perspective: over the training process. training and learning process. observing the learning dynamics of nlp models is not a new approach. it has been adopted for interpretability analysis to probe when and how linguistic capabilities emerge within language models @xcite , or inspect which features may be "harder" to learn @xcite . with respect to analyses on a single snapshot, a diachronic perspective has the advantage of accounting for the evolution of nlp capabilities, making them more transparent based on trends' observation. such an understanding can then be turned into actionable improvements. accordingly, @xcite looked at the time-wise development of different linguistic abilities in mt, so to inform distillation practices and improve the performance of their systems. additionally, the studies by @xcite on the learning dynamics of extrasentential phenomena highlighted how stopping criteria based on bleu @xcite are unreliable for context-aware mt. finally, @xcite observed the evolution of different linguistic phenomena in system's output, noting how some of them seem to actually worsen across iterations. overall, as @xcite noted, not much effort has been put into investigating how the training process evolves with regards to measurable factors of translation quality, such as linguistic criteria (grammar, syntax, semantics). we aim to fill this gap by evaluating gender translation of different st systems at all training checkpoints. ## qualitative insights we conclude our analysis with a manual inspection of the outputs of our st systems at two i) initial, ii) middle, and ii) final ckps of their training process. to this aim, we opt for the en-es language pair -for which we observed the highest bleu and gender coverage scores ( in the first two ckps of both models, the output has very low quality. 18 it is characterized by extensive repetitions of frequent words, like mother (madre in a) or young (joven, jóvenes in b-g). also, whereas functional words foot_18 are already appropriately employed and inflected with the correct gender (e.g. a @xmath1, a,g), the noun master is not learnt yet and remains out of coverage; notably, bpe generates the word hombre (i.e. man) instead. interestingly, if we also look at the gender cue noun sister, it maps to another kinship term daughter for bpe (g), whereas char generalizes target morphology over english lexicon at these stages (sistería in a, sistera in b). such lexical issues are all refined by the middle ckps, where the systems have acquired both sister/hermana (with the feminine inflected adjective antigua foot_19 in c) and master/maestr*, which in this case undergoes an interesting gender evolution across systems. for char, we assist to an adjustment from masculine inflection (b), to a feminine one (c onwards) that remains stable until the end of training, even after the best ckp. instead, bpe has a reversed trend. in h, the output una maestra reveals that this system can and has learnt to generate feminine forms. however, as the training progresses it switches to m inflections and never rebounds to the f ones. rather, in the last epochs k-l it produces alternative synonyms, but always with the wrong gender (amo, dueño). 21 overall, this case exemplifies how, in spite of i) having f morphological capabilities, and ii) the presence of a close cue disambiguating gender, the bpe system confidently relies on spurious and irrelevant patterns for gender translation. ## limitations and future work in this work, we rendered the st training process less opaque by analyzing the learning process of gender. to do so, we looked into st outputs. however, a complementary perspective would be to rely on explainability and probing approaches on system's inner mechanisms @xcite and verify their compatibility with our findings. also, a contrastive comparison of the learning curves for gender and other linguistic phenomena implying a one-to-many mapping (e.g. politeness @xmath2: tu/usted) could pinpoint learning trends which are specific to gender bias. a limit of our analyses is that they include only 5 epochs after their best validation loss. in light of our a posteriori finding that f gender -especially for char -does not reach a plateau in the last epochs, future work is needed to confirm whether and to what extent f learning keeps improving. this could inform studies on i) how to leverage diversified output to alleviate gender bias in our models, ii) gender-sensitive stopping criteria. finally, we point out that for the most fine-grained level of analyses (sec. 5.4), our evaluation is based on very specific subsets (e.g. nouns broke down into 1f, 2f, 1m, 2m). 22 this comes with an inherent reduction of the amount of 21 although inappropriate in this context, both amo and dueño are valid mapping to the word master). 22 in the appendix, we provide must-she statistics ( note that our study lies on the specificity of three comparable grammatical gender languages. we are thus cautious about generalizing our findings. experiments on other training sets and language pairs are currently hindered by the lack of an available natural, gender-sensitive st benchmark that covers alternative gender directions. while bearing this in mind, we however underscore that the conditions of gender translation significantly change depending on the features of the accounted languages and direction (e.g. translating from grammatical gender languages to english and not vice versa). thus, gender phenomena on typologically different gendered languages would not be directly comparable and compatible with the presented analyses. rather than a specific limitation of our setting, we regard this as an intrinsic condition. ## conclusion despite the mounting evidence of biased behaviour in language technologies, its understanding is hindered by the complex and opaque nature of current neural approaches. in this work, we shed light on the emergence of gender bias in st systems by focusing on their learning dynamics over training. in this way, we adopt a new perspective that accounts for the time-wise appearance of gender capabilities, and examine their stability, reliability and course of development. for three language pairs (@xmath3, fr, it) we inspect the learning curves of feminine and masculine gender translation i) at several levels of granularity; ii) with respect to progress in terms of overall translation quality; iii) on the output of st systems trained on target data segmented as either character or sub-words units (bpe). in our diachronic analysis, we unveil that i) feminine gender is learnt late over the course of training, ii) it never reaches a plateau within the number of iterations required for model convergence at training time, and iii) its refinements are concealed by standard evaluation metrics. also, by looking at the stability vs. fluctuations of the explored trends, we identify under which circumstances st models seem to actually progressively acquire feminine and masculine translation, and when instead their erratic, antiphase behavior reflects unreliable choices made by the systems. in this way, we find that nounsthe lexical category most impacted by gender bias -present a firm and huge gender divide over the whole training, where st systems do not rely on relevant information to support feminine translation and never really adjust its generation.