Papers by Maureen de Seyssel
Toward Machine Interpreting: Lessons from Human Interpreting Studies (2025.emnlp-main)
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| Challenge: | Current speech translation systems are static and do not adapt to real-world situations in ways human interpreters do. |
| Approach: | They propose to model human interpreting using a new language model to improve usability . they argue that there is great potential to adopt many human interpreted principles . |
| Outcome: | The proposed models can be used to improve human interpreting and improve translation performance. |
Assessing the Role of Data Quality in Training Bilingual Language Models (2025.findings-emnlp)
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| Challenge: | a recent study shows that adding more languages can degrade performance for some languages while improving others. |
| Approach: | They propose a data filtering strategy to select high-quality bilingual training data with only high quality English data. |
| Outcome: | The proposed approach improves bilingual model performance by 2–4% and reduces bilingual models performance gaps to 1%. |
The Role of Prosody in Spoken Question Answering (2025.findings-naacl)
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| Challenge: | lexical information is not available in most models, but prosody is important in understanding spoken language. |
| Approach: | They investigate the role of prosody in the process of answering a spoken question by isolating prosodic and lexical information from a natural speech dataset. |
| Outcome: | The proposed models can perform reasonably well on the SLUE-SQA-5 dataset, but when lexical information is available, models tend to predominantly rely on it. |
Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks (2025.emnlp-main)
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| Challenge: | Existing studies have shown that multilingual models encode languagespecific information and language-agnostic features, but the nature and interaction of these representations is not fully understood. |
| Approach: | They propose a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). |
| Outcome: | The proposed tasks show that language discrimination declines over training and strengthens over time and stabilizes in deeper layers. |