activity
20162023
most citedGoogle's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

5.7k citations · 11.2k across the 71 of their papers we have counts for

collaborators

75 papers

cs.CL2023★ 1 cited

SLM: Bridge the thin gap between speech and text foundation models

Mingqiu Wang, Wei Han, Izhak Shafran +15

We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM…

eess.AS2023★ 4 cited

Efficient Adapters for Giant Speech Models

Nanxin Chen, Izhak Shafran, Yu Zhang +4

Large pre-trained speech models are widely used as the de-facto paradigm, especially in scenarios when there is a limited amount of labeled data available. However, finetuning all…

cs.CL2023★ 168 cited

PaLM 2 Technical Report

Rohan Anil, Andrew M. Dai, Orhan Firat +125

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…

cs.CV2023★ 2 cited

VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining

Junjie Ke, Keren Ye, Jiahui Yu +3

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic…

cs.CL2023★ 113 cited

Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

Yu Zhang, Wei Han, James Qin +24

We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the enc…

cs.CL2022

AnyTOD: A Programmable Task-Oriented Dialog System

Jeffrey Zhao, Yuan Cao, Raghav Gupta +6

We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of handling unseen tasks without task-specific training. We view TOD as a program executed by…