no code implementations • WMT (EMNLP) 2020 • Lei Yu, Laurent Sartran, Po-Sen Huang, Wojciech Stokowiec, Domenic Donato, Srivatsan Srinivasan, Alek Andreev, Wang Ling, Sona Mokra, Agustin Dal Lago, Yotam Doron, Susannah Young, Phil Blunsom, Chris Dyer
This paper describes the DeepMind submission to the Chinese\rightarrowEnglish constrained data track of the WMT2020 Shared Task on News Translation.
3 code implementations • 7 Aug 2023 • Miloš Stanojević, Laurent Sartran
The development of deep learning software libraries enabled significant progress in the field by allowing users to focus on modeling, while letting the library to take care of the tedious and time-consuming task of optimizing execution for modern hardware accelerators.
2 code implementations • 12 May 2023 • Emanuele Bugliarello, Laurent Sartran, Aishwarya Agrawal, Lisa Anne Hendricks, Aida Nematzadeh
While pretraining on large-scale image-text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, recent work has demonstrated that pretrained models lack "fine-grained" understanding, such as the ability to recognise relationships, verbs, and numbers in images.
Ranked #13 on Visual Reasoning on Winoground
no code implementations • 28 Nov 2022 • Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler
Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement.
no code implementations • 1 Mar 2022 • Laurent Sartran, Samuel Barrett, Adhiguna Kuncoro, Miloš Stanojević, Phil Blunsom, Chris Dyer
We find that TGs outperform various strong baselines on sentence-level language modeling perplexity, as well as on multiple syntax-sensitive language modeling evaluation metrics.
no code implementations • ICLR 2022 • Wang Ling, Wojciech Stokowiec, Domenic Donato, Laurent Sartran, Lei Yu, Austin Matthews, Chris Dyer
When applied to autoregressive models, our algorithm has different biases than beam search has, which enables a new analysis of the role of decoding bias in autoregressive models.
no code implementations • ICLR 2021 • Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick, David Raposo
We show that EPNs learn to execute a value iteration-like planning algorithm and that they generalize to situations beyond their training experience.
no code implementations • TACL 2020 • Lei Yu, Laurent Sartran, Wojciech Stokowiec, Wang Ling, Lingpeng Kong, Phil Blunsom, Chris Dyer
We show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a compelling benefit as parallel documents are not always available.
no code implementations • 25 Sep 2019 • Lei Yu, Laurent Sartran, Wojciech Stokowiec, Wang Ling, Lingpeng Kong, Phil Blunsom, Chris Dyer
We show that Bayes' rule provides a compelling mechanism for controlling unconditional document language models, using the long-standing challenge of effectively leveraging document context in machine translation.