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Ioannis Tsiamas

8 accepted papers

2025

BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation

Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-juss{\`a}, Joe Chuang, David Dale +13
EMNLP 2025

BOUQuET is a multi-way, multicentric and multi-register/domain dataset and benchmark, and a broader collaborative initiative. This dataset is handcrafted in 8 non-English languages (i.e. Egyptian Arabic and Modern Standard Arabic, French, German, Hindi, Indonesian, Mandarin Chinese, Russian, and Spa

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Improving Language and Modality Transfer in Translation by Character-level Modeling

Ioannis Tsiamas, David Dale, Marta R. Costa-jussà
ACL 2025long

Current translation systems, despite being highly multilingual, cover only 5% of the world’s languages. Expanding language coverage to the long-tail of low-resource languages requires data-efficient methods that rely on cross-lingual and cross-modal knowledge transfer. To this end, we propose a char…

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2024

Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity

Santiago Pascual, Chunghsin YEH*, Ioannis Tsiamas, Joan Serrà
ECCV 2024poster

"Video-to-audio (V2A) generation leverages visual-only video features to render plausible sounds that match the scene. Importantly, the generated sound onsets should match the visual actions that are aligned with them, otherwise unnatural synchronization artifacts arise. Recent works have explored t…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗
2024

Pushing the Limits of Zero-shot End-to-End Speech Translation

Ioannis Tsiamas, Gerard I. Gállego, José A. R. Fonollosa, Marta R. Costa-jussà
ACL 2024findings

Data scarcity and the modality gap between the speech and text modalities are two major obstacles of end-to-end Speech Translation (ST) systems, thus hindering their performance. Prior work has attempted to mitigate these challenges by leveraging external MT data and optimizing distance metrics that…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2023

Efficient Speech Translation with Dynamic Latent Perceivers

Ioannis Tsiamas, Gerard I. Gállego, José A. R. Fonollosa, Marta R. Costa-jussà
ICASSP 2023accepted

Transformers have been the dominant architecture for Speech Translation in recent years, achieving significant improvements in translation quality. Since speech signals are longer than their textual counterparts, and due to the quadratic complexity of the Transformer, a down-sampling step is essenti…

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2023

Explaining How Transformers Use Context to Build Predictions

Javier Ferrando, Gerard I. Gállego, Ioannis Tsiamas, Marta R. Costa-jussà
ACL 2023long

Language Generation Models produce words based on the previous context. Although existing methods offer input attributions as explanations for a model’s prediction, it is still unclear how prior words affect the model’s decision throughout the layers. In this work, we leverage recent advances in exp…

Cited by 33Source ↗PDF ↗code ↗Scholar ↗
2023

SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations

Ioannis Tsiamas, José A.R. Fonollosa, Marta R. Costa-jussà
EMNLP 2023long findings

End-to-end Speech Translation is hindered by a lack of available data resources. While most of them are based on documents, a sentence-level version is available, which is however single and static, potentially impeding the usefulness of the data. We propose a new data augmentation strategy, SegAugm…

Cited by 0Source ↗code ↗Scholar ↗