WinoST

The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. Evaluating the biases should be an essential step towards mitigating them in the systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT, an MT challenge set, and both follow an evaluation protocol to measure gender accuracy. Using an S-Transformer end-to-end speech translation system, we report the gender bias evaluation on four language pairs, and we reveal the inaccuracies in translations generating gender-stereotyped translations.

Trustworthy AI Relevance

This metric addresses Robustness and Fairness by quantifying relevant system properties. Robustness: WinoST measures a model's ability to correctly resolve pronouns/antecedents in Winograd-style contexts that require commonsense/world knowledge rather than superficial cues. High performance indicates greater resilience to linguistically challenging or adversarial-like inputs and thus supports evaluation of model reliability under difficult or distribution-shift conditions.