Papers by Matthieu Futeral
Towards Zero-Shot Multimodal Machine Translation (2025.findings-naacl)
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| Challenge: | Current multimodal machine translation systems rely on fully supervised data, which is costly to collect and prevents extension of MMT to language pairs with no such data. |
| Approach: | They propose a method to bypass the need for fully supervised data to train MMT systems . they adapt a strong text-only machine translation model to a visually conditioned language model and a divergence test set to evaluate how well models use images to disambiguate translations. |
| Outcome: | The proposed method can generalize to languages with no fully supervised training data. |
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell (2020.acl-main)
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Djamé Seddah, Farah Essaidi, Amal Fethi, Matthieu Futeral, Benjamin Muller, Pedro Javier Ortiz Suárez, Benoît Sagot, Abhishek Srivastava
| Challenge: | a treebank for a north-African Arabic dialect known for code-switching is made freely available . authors: geopolitical events are a factor highlighting a language deficiency in terms of natural language processing resources . |
| Approach: | They propose to make a treebank for a romanized user-generated content variety of Algerian . they supplement it with 50k unlabeled sentences from common crawl and web-crawled data . |
| Outcome: | The proposed treebank is made of 1500 sentences, fully annotated in morpho-syntax and universal dependency syntax, with full translation at both the word and sentence levels. |
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus (2025.findings-acl)
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Matthieu Futeral, Armel Randy Zebaze, Pedro Ortiz Suarez, Julien Abadji, Rémi Lacroix, Cordelia Schmid, Rachel Bawden, Benoît Sagot
| Challenge: | Existing studies show that multimodal large language models can learn from text-image data. |
| Approach: | They propose to train multimodal large language models on large amounts of text-image data . they also show a boost in few-shot learning performance across various multilingual tasks . |
| Outcome: | The proposed dataset is not public and is only in English . it is the first large-scale multilingual and multimodal document corpus crawled from the web. |
MAD Speech: Measures of Acoustic Diversity of Speech (2025.naacl-long)
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| Challenge: | Recent advances in generative spoken language modeling have produced models that produce speech in a wide range of voices, prosody and recording conditions. |
| Approach: | They propose acoustic diversity metrics that measure voice, gender, emotion, accent, background noise and a priori known diversity preferences for each facet. |
| Outcome: | The proposed metrics show that they achieve stronger agreement with diversity than baselines. |
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation (2023.acl-long)
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| Challenge: | Recent work in multimodal machine translation (MT) has shown that ambiguity can be resolved using accompanying context such as images. |
| Approach: | They propose a multimodal machine translation approach based on a strong text-only MT model and a novel guided self-attention mechanism to train it. |
| Outcome: | The proposed model outperforms existing models on EnglishFrench, EnglishGerman and EnglishCzech benchmarks and is freely available. |