[Colloquium] REMINDER: 6/19 TTIC Colloquium @ 10am: Michael Schuster, Google

[Mary Marre via Colloquium]

*PLEASE NOTE SPECIAL TIME!!!!*

When:      Monday, June 19th at *10:00 a.m. *

Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526

Who:       Michael Schuster, Google


Title:
The Move to Neural Machine Translation at Google

Abstract:
Machine learning and in particular neural networks have made great advances
in the last few years for products that are used by millions of people,
most notably in speech recognition, image recognition and most recently in
neural machine translation. 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 both in training and in translation inference. Also, most NMT
systems have difficulty with rare words. These issues have hindered NMT's
use in practical deployments and services, where both accuracy and speed
are essential. In this work, we present GNMT, Google's Neural Machine
Translation system, which addresses many of these issues. The model
consists of a deep LSTM network with 8 encoder and 8 decoder layers using
attention and residual connections. To accelerate final translation speed,
we employ low-precision arithmetic during inference computations. To
improve handling of rare words, we divide words into a limited set of
common sub-word units for both input and output. On the WMT'14
English-to-French and English-to-German benchmarks, GNMT achieves
competitive results to state-of-the-art. Using human side-by-side
evaluations it reduces translation errors by more than 60% compared to
Google's phrase-based production system. The new Google Translate was
launched in late 2016 and has improved translation quality significantly
for all Google users.

Bio:
Dr. Mike Schuster graduated in Electric Engineering from the
Gerhard-Mercator University in Duisburg, Germany in 1993. After receiving a
scholarship he spent a year in Japan to study Japanese in Kyoto and Fiber
Optics in the Kikuchi laboratory at Tokyo University. His professional
career in machine learning and speech brought him to Advanced
Telecommunications Research Laboratories in Kyoto, Nuance in the US and NTT
in Japan where he worked on general machine learning and speech recognition
research and development after getting his PhD at the Nara Institute of
Science and Technology. Dr. Schuster joined the Google speech group in the
beginning of 2006, seeing speech products being developed from scratch to
toy demos to serving millions of users in many languages over the next
eight years, and he was the main developer of the original Japanese and
Korean speech recognition models. He is now part of the Google Brain group
which focuses on building large-scale neural network and machine learning
infrastructure for Google and has been working on infrastructure with the
TensorFlow toolkit as well as on research, mostly in the field of speech
and translation with various types of recurrent neural networks. In 2016 he
led the development of the new Google Neural Machine Translation system,
which reduced translation errors by more than 60% compared to the previous
system.
Hosts: Sadaoki Furui <furui at ttic.edu> and Karen Livescu <klivescu at ttic.edu>


For more information on the colloquium series or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php


Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Sun, Jun 18, 2017 at 8:13 PM, Mary Marre <mmarre at ttic.edu> wrote:

> *PLEASE NOTE SPECIAL TIME!!!!*
>
> When:      Monday, June 19th at *10:00 a.m. *
>
> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Michael Schuster, Google
>
>
> Title:
> The Move to Neural Machine Translation at Google
>
> Abstract:
> Machine learning and in particular neural networks have made great
> advances in the last few years for products that are used by millions of
> people, most notably in speech recognition, image recognition and most
> recently in neural machine translation. 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 both in training and in translation inference.
> Also, most NMT systems have difficulty with rare words. These issues have
> hindered NMT's use in practical deployments and services, where both
> accuracy and speed are essential. In this work, we present GNMT, Google's
> Neural Machine Translation system, which addresses many of these issues.
> The model consists of a deep LSTM network with 8 encoder and 8 decoder
> layers using attention and residual connections. To accelerate final
> translation speed, we employ low-precision arithmetic during inference
> computations. To improve handling of rare words, we divide words into a
> limited set of common sub-word units for both input and output. On the
> WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves
> competitive results to state-of-the-art. Using human side-by-side
> evaluations it reduces translation errors by more than 60% compared to
> Google's phrase-based production system. The new Google Translate was
> launched in late 2016 and has improved translation quality significantly
> for all Google users.
>
> Bio:
> Dr. Mike Schuster graduated in Electric Engineering from the
> Gerhard-Mercator University in Duisburg, Germany in 1993. After receiving a
> scholarship he spent a year in Japan to study Japanese in Kyoto and Fiber
> Optics in the Kikuchi laboratory at Tokyo University. His professional
> career in machine learning and speech brought him to Advanced
> Telecommunications Research Laboratories in Kyoto, Nuance in the US and NTT
> in Japan where he worked on general machine learning and speech recognition
> research and development after getting his PhD at the Nara Institute of
> Science and Technology. Dr. Schuster joined the Google speech group in
> the beginning of 2006, seeing speech products being developed from scratch
> to toy demos to serving millions of users in many languages over the next
> eight years, and he was the main developer of the original Japanese and
> Korean speech recognition models. He is now part of the Google Brain group
> which focuses on building large-scale neural network and machine learning
> infrastructure for Google and has been working on infrastructure with the
> TensorFlow toolkit as well as on research, mostly in the field of speech
> and translation with various types of recurrent neural networks. In 2016 he
> led the development of the new Google Neural Machine Translation system,
> which reduced translation errors by more than 60% compared to the previous
> system.
> Hosts: Sadaoki Furui <furui at ttic.edu> and Karen Livescu
> <klivescu at ttic.edu>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list, please see http://www.ttic.edu/colloquium.php
>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Fri, Jun 16, 2017 at 1:15 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> *PLEASE NOTE SPECIAL TIME!!!!*
>>
>> When:      Monday, June 19th at *10:00 a.m. *
>>
>> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:       Michael Schuster, Google
>>
>>
>> Title:
>> The Move to Neural Machine Translation at Google
>>
>> Abstract:
>> Machine learning and in particular neural networks have made great
>> advances in the last few years for products that are used by millions of
>> people, most notably in speech recognition, image recognition and most
>> recently in neural machine translation. 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 both in training and in translation inference.
>> Also, most NMT systems have difficulty with rare words. These issues have
>> hindered NMT's use in practical deployments and services, where both
>> accuracy and speed are essential. In this work, we present GNMT, Google's
>> Neural Machine Translation system, which addresses many of these issues.
>> The model consists of a deep LSTM network with 8 encoder and 8 decoder
>> layers using attention and residual connections. To accelerate final
>> translation speed, we employ low-precision arithmetic during inference
>> computations. To improve handling of rare words, we divide words into a
>> limited set of common sub-word units for both input and output. On the
>> WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves
>> competitive results to state-of-the-art. Using human side-by-side
>> evaluations it reduces translation errors by more than 60% compared to
>> Google's phrase-based production system. The new Google Translate was
>> launched in late 2016 and has improved translation quality significantly
>> for all Google users.
>>
>> Bio:
>> Dr. Mike Schuster graduated in Electric Engineering from the
>> Gerhard-Mercator University in Duisburg, Germany in 1993. After receiving a
>> scholarship he spent a year in Japan to study Japanese in Kyoto and Fiber
>> Optics in the Kikuchi laboratory at Tokyo University. His professional
>> career in machine learning and speech brought him to Advanced
>> Telecommunications Research Laboratories in Kyoto, Nuance in the US and NTT
>> in Japan where he worked on general machine learning and speech recognition
>> research and development after getting his PhD at the Nara Institute of
>> Science and Technology. Dr. Schuster joined the Google speech group in
>> the beginning of 2006, seeing speech products being developed from scratch
>> to toy demos to serving millions of users in many languages over the next
>> eight years, and he was the main developer of the original Japanese and
>> Korean speech recognition models. He is now part of the Google Brain group
>> which focuses on building large-scale neural network and machine learning
>> infrastructure for Google and has been working on infrastructure with the
>> TensorFlow toolkit as well as on research, mostly in the field of speech
>> and translation with various types of recurrent neural networks. In 2016 he
>> led the development of the new Google Neural Machine Translation system,
>> which reduced translation errors by more than 60% compared to the previous
>> system.
>> Hosts: Sadaoki Furui <furui at ttic.edu> and Karen Livescu
>> <klivescu at ttic.edu>
>>
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list, please see http://www.ttic.edu/colloquium.php
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 504*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757 <(773)%20834-1757>*
>> *f: (773) 357-6970 <(773)%20357-6970>*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>> On Fri, Jun 9, 2017 at 10:28 AM, Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *PLEASE NOTE SPECIAL TIME!!!!*
>>>
>>> When:      Monday, June 19th at *10:00 a.m. *
>>>
>>> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>>
>>> Who:       Michael Schuster, Google
>>>
>>>
>>> Title:
>>> The Move to Neural Machine Translation at Google
>>>
>>> Abstract:
>>> Machine learning and in particular neural networks have made great
>>> advances in the last few years for products that are used by millions of
>>> people, most notably in speech recognition, image recognition and most
>>> recently in neural machine translation. 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 both in training and in translation inference.
>>> Also, most NMT systems have difficulty with rare words. These issues have
>>> hindered NMT's use in practical deployments and services, where both
>>> accuracy and speed are essential. In this work, we present GNMT, Google's
>>> Neural Machine Translation system, which addresses many of these issues.
>>> The model consists of a deep LSTM network with 8 encoder and 8 decoder
>>> layers using attention and residual connections. To accelerate final
>>> translation speed, we employ low-precision arithmetic during inference
>>> computations. To improve handling of rare words, we divide words into a
>>> limited set of common sub-word units for both input and output. On the
>>> WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves
>>> competitive results to state-of-the-art. Using human side-by-side
>>> evaluations it reduces translation errors by more than 60% compared to
>>> Google's phrase-based production system. The new Google Translate was
>>> launched in late 2016 and has improved translation quality significantly
>>> for all Google users.
>>>
>>> Bio:
>>> Dr. Mike Schuster graduated in Electric Engineering from the
>>> Gerhard-Mercator University in Duisburg, Germany in 1993. After receiving a
>>> scholarship he spent a year in Japan to study Japanese in Kyoto and Fiber
>>> Optics in the Kikuchi laboratory at Tokyo University. His professional
>>> career in machine learning and speech brought him to Advanced
>>> Telecommunications Research Laboratories in Kyoto, Nuance in the US and NTT
>>> in Japan where he worked on general machine learning and speech recognition
>>> research and development after getting his PhD at the Nara Institute of
>>> Science and Technology. Dr. Schuster joined the Google speech group in
>>> the beginning of 2006, seeing speech products being developed from scratch
>>> to toy demos to serving millions of users in many languages over the next
>>> eight years, and he was the main developer of the original Japanese and
>>> Korean speech recognition models. He is now part of the Google Brain group
>>> which focuses on building large-scale neural network and machine learning
>>> infrastructure for Google and has been working on infrastructure with the
>>> TensorFlow toolkit as well as on research, mostly in the field of speech
>>> and translation with various types of recurrent neural networks. In 2016 he
>>> led the development of the new Google Neural Machine Translation system,
>>> which reduced translation errors by more than 60% compared to the previous
>>> system.
>>> Hosts: Sadaoki Furui <furui at ttic.edu> and Karen Livescu
>>> <klivescu at ttic.edu>
>>>
>>>
>>> For more information on the colloquium series or to subscribe to the
>>> mailing list, please see http://www.ttic.edu/colloquium.php
>>>
>>>
>>>
>>>
>>>
>>> Mary C. Marre
>>> Administrative Assistant
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Room 504*
>>> *Chicago, IL  60637*
>>> *p:(773) 834-1757 <(773)%20834-1757>*
>>> *f: (773) 357-6970 <(773)%20357-6970>*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
>>
>
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