Analyzing Gender Translation Errors to Identify Information Flows between the Encoder and Decoder of a NMT System (2022)

Abstract: Multiple studies have shown that existing NMT systems demonstrate some kind of “gender bias”. As a result, MT output appears to err more often for feminine forms and to amplify social gender misrepresentations, which is potentially harmful to users and practioners of these technologies. This paper continues this line of investigations and reports results obtained with a new test set in strictly controlled conditions. This setting allows us to better understand the multiple inner mechanisms that are causing these biases, which include the linguistic expressions of gender, the unbalanced distribution of masculine and feminine forms in the language, the modelling of morphological variation and the training process dynamics. To counterbalance these effects, we formulate several proposals and notably show that modifying the training loss can effectively mitigate such biases.

## introduction state-of-the-art machine translation models (tms) have been shown to suffer from genderbias @xcite and works trying to mitigate this problem constitute a very active line of research (e.g. costa-jussà and de jorge (2020); saunders and byrne (2020); @xcite ). we here adopt a different point of view and try to understand why the tm, often incorrectly, chooses a masculine rather than a feminine form. for this, we identify the mechanisms which the neural network uses to extract gender information from the source side and transfer it to the target side. this study of the causes of gender bias illustrates in a more general way the inner working of neural translation systems and notably reveals information flows between the encoder and the decoder involved in a tm. to study gender transfer, we introduce a new french-english test set specifically designed to highlight the difficulties of translating gender information between these two languages. using a controlled test set allows us to precisely pinpoint where and how gender is expressed in source and target sentences and to quantify the information flow in an encoder-decoder architecture. our experiments rely on both well-known methods such as probing or new methods tailored to identify gender bias such as comparing the predictions of a language model and those of a tm to better analyze the possible causes of gender bias. considering a controlled experimental setting also allows us to assess the impact of training conditions such as subword segmentation or training data distributions. the rest of the paper is organized as follows. in §2, we describe our test set and use it to highlight gender bias in state-of-the-art systems. in §3 we describe several experiments aiming to study how information flows between the encoder and the decoder can explain these biases. to counterbalance these effects, we formulate several proposals in §4, and notably show that a simple modification of the training loss can effectively mitigate gender bias. ## uncovering the flows of gender information in this section, we report experiments aimed to explain the results reported in ## conclusions we have presented a new series of experimental evidence highlighting the causes of gender biases in nmt. our analyses are based on observing pronoun translation errors in a controlled setting, using a new french-english test set of more than 3,000 occupational nouns. they mostly confirm the findings of previous studies that have amply documented the fact that errors were more likely to occur for feminine than for masculine pronouns, the latter being used as the default option in most context. additional analyses based (a) on sys-tematic probes, (b) on the comparison of lm vs. tm probabilities, (c) on subword splitting, (d) on the gradient flow in the encoder and decoder layers show that the cause of these biases are multifactorial. we have finally proposed a new way to mitigate these errors via a margin augmented training loss, specifically aimed at improving the information flow between source and target. in our future work, we intend to continue exploring the potential of margin augmented losses, with the aim to also train the cost matrix, and to perform more systematic experiments with other systems and language pairs. another line of investigation will be considering other linguistic phenomena posing difficult challenges for mt systems, such as the prediction of tense @xcite or mood information @xcite .