Maxim Krikun

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

2016 · arXiv (Cornell University)

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive …

Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

2017 · Transactions of the Association for Computational Linguistics

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead …

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

2019 · arXiv (Cornell University)

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, …

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

2016 · arXiv (Cornell University)

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces …

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

2019 · arXiv (Cornell University)

We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model …

LaMDA: Language Models for Dialog Applications

2022 · arXiv (Cornell University)

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog …

Gemini: A Family of Highly Capable Multimodal Models

2023 · arXiv (Cornell University)

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging …