Papers by Dennis Fucci
Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
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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. |
| Approach: | They propose to use probing methods to assess gender encoding across ST models. |
| Outcome: | The proposed models capture speaker-specific features, including gender, while older models do not . low gender encoding capabilities result in systems’ tendency toward a masculine default, a translation bias that is more pronounced in newer architectures. |
Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus (2023.emnlp-main)
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| Challenge: | Societal gender asymmetries and inequalities are perpetuated through language . MT often defaults to masculine representations by making undue binary gender assumptions . |
| Approach: | They propose a benchmark and automated evaluation methods to assess gender-neutral translation from English to Italian. |
| Outcome: | The proposed method is based on a survey on gender-neutral translation. |
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. |
Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gaps (2024.emnlp-main)
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| Challenge: | a new class of multitasks, multilingual neural networks, has recently pushed the boundaries of speech-related tasks. |
| Approach: | They evaluate performance of two widely used multilingual automatic speech recognition models . they find clear gender disparities, with the advantaged group varying across languages . |
| Outcome: | The proposed models are compared on 19 languages from eight language families and two speaking conditions. |
A Prompt Response to the Demand for Automatic Gender-Neutral Translation (2024.eacl-short)
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| Challenge: | Advancements in machine translation (MT) are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. |
| Approach: | They propose to use GPT-4 to generate GNTs that avoid bias and undue binary assumptions by comparing MT with the popular GPT-3 model. |
| Outcome: | The proposed model outperforms the existing model and provides valuable insights into the potential and challenges associated with prompting for neutrality. |