563 citations · 646 across the 28 of their papers we have counts for
7 papers · 1 filter
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…