Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection (2023.emnlp-main)
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| Challenge: | Existing solutions to control speaker-related gender inflections in ST involve dedicated model retraining on gender-labeled data. |
| Approach: | They propose to use a gender-based inference-time solution to control speaker-related gender inflections in ST by replacing the implicitly learned internal language model with gender-specific external LMs. |
| Outcome: | The proposed approach outperforms the base models and the best training-time mitigation strategy by up to 31.0 and 1.6 points in gender accuracy, respectively, for feminine forms. |
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| Challenge: | Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. |
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| Challenge: | Recent studies have focused on gender bias in neural machine translation (NMT) incorrectly gendered translations can reflect or amplify social biases. |
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