563 citations · 646 across the 28 of their papers we have counts for
31 papers · 1 filter
Improving Term Evaluation in Machine Translation: Variation Matters
Nicolas Dahan, Ziqian Peng, François Yvon +1
Terminology evaluation in machine translation (MT) usually assumes a single correct target form per source term. However, human translators routinely introduce variation that curre…
Translation Heads: Disentangling meaning from language in LLM-based machine translation
Théo Lasnier, Armel Zebaze, Djamé Seddah +2
Mechanistic Interpretability (MI) seeks to explain how neural networks implement their capabilities, but the scale of Large Language Models (LLMs) has limited prior MI work in Mach…
When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content
Lydia Nishimwe, Benoît Sagot, Rachel Bawden
User-generated content (UGC) is characterised by frequent use of non-standard language, from spelling errors to expressive choices such as slang, character repetitions, and emojis.…
Gaperon: A Peppered English-French Generative Language Model Suite
Nathan Godey, Wissam Antoun, Rian Touchent +4
We release Gaperon, a fully open suite of French-English-coding language models designed to advance transparency and reproducibility in large-scale model training. The Gaperon fami…
LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens
Armel Zebaze, Rachel Bawden, Benoît Sagot
Large reasoning models (LRMs) have led to new possibilities in terms of problem-solving, through the devising of a natural language thought process prior to answering a query. Whil…
TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation
Armel Zebaze, Benoît Sagot, Rachel Bawden
LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning (ICL), rivaling supervised models when translating into high-resource languages…
A French Version of the OLDI Seed Corpus
Malik Marmonier, Benoît Sagot, Rachel Bawden
We present the first French partition of the OLDI Seed Corpus, our submission to the WMT 2025 Open Language Data Initiative (OLDI) shared task. We detail its creation process, whic…
Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation
Armel Zebaze, Benoît Sagot, Rachel Bawden
The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural…
Explicit Learning and the LLM in Machine Translation
Malik Marmonier, Rachel Bawden, Benoît Sagot
This study explores an LLM's ability to learn new languages using explanations found in a grammar book, a process we term "explicit learning." To rigorously assess this ability, we…
AFRIDOC-MT: Document-level MT Corpus for African Languages
Jesujoba O. Alabi, Israel Abebe Azime, Miaoran Zhang +13
This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yorùbá, and Zulu. The da…
Investigating Length Issues in Document-level Machine Translation
Ziqian Peng, Rachel Bawden, François Yvon
Transformer architectures are increasingly effective at processing and generating very long chunks of texts, opening new perspectives for document-level machine translation (MT). I…
Tree of Problems: Improving structured problem solving with compositionality
Armel Zebaze, Benoît Sagot, Rachel Bawden
Large Language Models (LLMs) have demonstrated remarkable performance across multiple tasks through in-context learning. For complex reasoning tasks that require step-by-step think…
In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation
Armel Zebaze, Benoît Sagot, Rachel Bawden
The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural…
Preliminary WMT24 Ranking of General MT Systems and LLMs
Tom Kocmi, Eleftherios Avramidis, Rachel Bawden +18
This is the preliminary ranking of WMT24 General MT systems based on automatic metrics. The official ranking will be a human evaluation, which is superior to the automatic ranking…
Towards Zero-Shot Multimodal Machine Translation
Matthieu Futeral, Cordelia Schmid, Benoît Sagot +1
Current multimodal machine translation (MMT) systems rely on fully supervised data (i.e models are trained on sentences with their translations and accompanying images). However, t…
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus
Matthieu Futeral, Armel Zebaze, Pedro Ortiz Suarez +5
Multimodal Large Language Models (mLLMs) are trained on a large amount of text-image data. While most mLLMs are trained on caption-like data only, Alayrac et al. (2022) showed that…
Making Sentence Embeddings Robust to User-Generated Content
Lydia Nishimwe, Benoît Sagot, Rachel Bawden
NLP models have been known to perform poorly on user-generated content (UGC), mainly because it presents a lot of lexical variations and deviates from the standard texts on which m…
Investigating Lexical Sharing in Multilingual Machine Translation for Indian Languages
Sonal Sannigrahi, Rachel Bawden
Multilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models,…
When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages
Niyati Bafna, Cristina España-Bonet, Josef van Genabith +2
Most existing approaches for unsupervised bilingual lexicon induction (BLI) depend on good quality static or contextual embeddings requiring large monolingual corpora for both lang…
Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM
Rachel Bawden, François Yvon
The NLP community recently saw the release of a new large open-access multilingual language model, BLOOM (BigScience et al., 2022) covering 46 languages. We focus on BLOOM's multil…
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation
Matthieu Futeral, Cordelia Schmid, Ivan Laptev +2
One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as images. However, recent work in multimodal…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification
Yu Lu Liu, Rachel Bawden, Thomas Scialom +2
In text summarization and simplification, system outputs must be evaluated along multiple dimensions such as relevance, factual consistency, fluency, and grammaticality, and a wide…
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
Simon Gabay, Pedro Ortiz Suarez, Alexandre Bartz +4
Language models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historica…