Papers by Jean Maillard
Multi-Task Retrieval for Knowledge-Intensive Tasks (2021.acl-long)
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Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, Gargi Ghosh
| Challenge: | Knowledge-intensive tasks require large amounts of knowledge about the world . recent neural retrieval models achieve better results by learning directly from task-specific training data. |
| Approach: | They propose a multi-task trained neural retrieval model that can be universally trained on a wide variety of problems. |
| Outcome: | The proposed model outperforms specialised retrievers on a few-shot setting and matches or improves state-of-the-art on multiple benchmarks. |
Conversational Semantic Parsing (2020.emnlp-main)
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Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Michael Haeger, Haoran Li, Yashar Mehdad, Veselin Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta
| Challenge: | Structured representations for task-oriented assistant systems are limited due to the limitations of the representation. |
| Approach: | They propose a semantic representation for task-oriented conversational systems that can represent co-reference and context carryover. |
| Outcome: | The proposed model improves the best results on ATIS, SNIPS, TOP and DSTC2 by up to 5 points for slot-carryover. |
Language-Aware Multilingual Machine Translation with Self-Supervised Learning (2023.findings-eacl)
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| Challenge: | Multilingual machine translation (MMT) is a challenging multitask optimization problem because of lack of a framework to learn language-specific parameters. |
| Approach: | They propose a self-supervised learning task that denies monolingual data to MMT . they then propose 'intra-distillation' task that co-trains with MMT task . |
| Outcome: | The proposed approach outperforms three state-of-the-art methods on 8-language and 15-language benchmarks. |
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)
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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger
| Challenge: | Language identification (LID) is a fundamental step in curating multilingual corpora. |
| Approach: | They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. |
| Outcome: | The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain. |
Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models (2020.tacl-1)
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| Challenge: | Existing research has focused on applying semantic models to decode brain activity associated with the meaning of individual words. |
| Approach: | They evaluate a range of semantic models to capture metaphor processing in the brain . they found that compositional models and word embeddings capture differences in the processing of literal and metaphoric sentences . |
| Outcome: | The proposed models capture differences in the processing of literal and metaphoric sentences, providing support for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension. |
OCR Improves Machine Translation for Low-Resource Languages (2022.findings-acl)
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| Challenge: | Despite many recent successes, Machine Translation still lacks support or fails to achieve good performance for most low-resource languages. |
| Approach: | They propose a benchmark to evaluate OCR systems on low-resource languages and low- resource scripts. |
| Outcome: | The proposed benchmark evaluates state-of-the-art OCR systems on low-resource languages and low-rural scripts. |
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models (P19-1)
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| Challenge: | Existing studies have shown that distributional semantic models can be used to decode fMRI patterns associated with specific aspects of semantic composition, such as the negation function. |
| Approach: | They apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. |
| Outcome: | The proposed models show reduced decoding of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. |
BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation (2025.emnlp-main)
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Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussà, Joe Chuang, David Dale, Mark Duppenthaler, Nathanial Paul Ekberg, Cynthia Gao, Daniel Edward Licht, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Ioannis Tsiamas, Arina Turkatenko, Albert Ventayol-Boada, Shireen Yates
| Challenge: | BOUQUET is a multi-way, multicentric and multi-register/domain dataset and benchmark . the dataset is handcrafted in 8 non-English languages . |
| Approach: | They propose to use BOUQuET to collect a multi-way, multicentric and multi-register/domain dataset and benchmark in 8 non-English languages. |
| Outcome: | The proposed dataset is available at https://huggingface.co/datasets/facebook/bouquet. |
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity (2023.findings-emnlp)
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| Challenge: | Recent studies have established that Mixture-of-experts models are parameter-inefficient as the improvement in performance diminishes with an increasing number of experts. |
| Approach: | They propose a mix-of-experts model that uses sparse activation to increase the number of parameters while maintaining low computational requirements per token. |
| Outcome: | The proposed models outperform state-of-the-art models on three multilingual machine translation benchmarks with 4, 15, and 94 language pairs. |
KILT: a Benchmark for Knowledge Intensive Language Tasks (2021.naacl-main)
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Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, Sebastian Riedel
| Challenge: | Existing models for knowledge-intensive language tasks require access to large, external knowledge sources. |
| Approach: | They propose a benchmark for knowledge-intensive language tasks (KILT) they test a shared dense vector index coupled with a seq2seq model to generate disambiguated text. |
| Outcome: | The proposed model outperforms tailor-made approaches on fact checking, open-domain question answering and dialog by generating disambiguated text. |
Toxicity in Multilingual Machine Translation at Scale (2023.findings-emnlp)
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Marta Costa-jussà, Eric Smith, Christophe Ropers, Daniel Licht, Jean Maillard, Javier Ferrando, Carlos Escolano
| Challenge: | In this paper, we evaluate and analyze added toxicity when translating a large dataset from English into 164 languages. |
| Approach: | They evaluate added toxicity when translating a large dataset from English into 164 languages. |
| Outcome: | The results show that added toxicity is more prevalent in low-resource languages than in high-resolution translations. |
Towards Privacy-Aware Sign Language Translation at Scale (2024.acl-long)
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| Challenge: | Existing sign language training systems require detailed and time aligned annotations to be effective. |
| Approach: | They propose a two-stage framework for privacy-aware SLT at scale that leverages self-supervised video pretraining on anonymized and unannotated videos followed by supervised SLT finetuning on a curated parallel dataset. |
| Outcome: | The proposed framework outperforms baselines on the How2Sign dataset and achieves state-of-the-art finetuned and zero-shot gloss-free SLT performance. |
2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset Download PDF (2025.findings-acl)
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Marta R. Costa-jussà, Bokai Yu, Pierre Andrews, Belen Alastruey, Necati Cihan Camgoz, Joe Chuang, Jean Maillard, Christophe Ropers, Arina Turkatenko, Carleigh Wood
| Challenge: | We extend the BELEBELE dataset to speech and sign, and extend the Automatic Speech Recognition Benchmark, FLEURS, by 20%. |
| Approach: | They extend the BELEBELE and FLEURS speech comprehension datasets to speech and sign . they evaluate the datasets for 5-shot and zero-shot settings and find that the accuracy is 10% lower than reading comprehension. |
| Outcome: | The proposed dataset covers 91 spoken languages and one sign language (ASL) it also extends the Automatic Speech Recognition Benchmark, FLEURS, by 20% across languages. |
Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation (2023.acl-long)
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Jean Maillard, Cynthia Gao, Elahe Kalbassi, Kaushik Ram Sadagopan, Vedanuj Goswami, Philipp Koehn, Angela Fan, Francisco Guzman
| Challenge: | Existing datasets are not economical to create large-scale datasets, but for low-resource languages, a few thousand professionally translated sentence pairs can be useful. |
| Approach: | They propose to use a dataset to train machine translation models on pre-existing and synthetic data to augment them with millions of sentences through backtranslation. |
| Outcome: | The proposed model can cover hundreds of languages with high quality training data even when smaller but lower quality datasets are used. |