activity
20162019
most citedMassively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

293 citations · 370 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL201926 cited

Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning

Yu Zhang, Ron J. Weiss, Heiga Zen +6

We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages. Moreover, the mode…

cs.CL2019293 cited

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

Naveen Arivazhagan, Ankur Bapna, Orhan Firat +10

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…

cs.CL201945 cited

Gmail Smart Compose: Real-Time Assisted Writing

Mia Xu Chen, Benjamin N Lee, Gagan Bansal +9

In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing…

eess.AS2017

An analysis of incorporating an external language model into a sequence-to-sequence model

Anjuli Kannan, Yonghui Wu, Patrick Nguyen +3

Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model comp…

cs.CL2017

No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models

Tara N. Sainath, Rohit Prabhavalkar, Shankar Kumar +9

For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-…

cs.CL2017

Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models

Rohit Prabhavalkar, Tara N. Sainath, Yonghui Wu +4

Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds t…