Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Optimizing Machine Translation by Le...
~
ProQuest Information and Learning Co.
Optimizing Machine Translation by Learning to Search.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Optimizing Machine Translation by Learning to Search./
Author:
Galron, Daniel.
Description:
1 online resource (218 pages)
Notes:
Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.
Subject:
Computer science. -
Online resource:
click for full text (PQDT)
ISBN:
9781267799647
Optimizing Machine Translation by Learning to Search.
Galron, Daniel.
Optimizing Machine Translation by Learning to Search.
- 1 online resource (218 pages)
Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.
Thesis (Ph.D.)--New York University, 2012.
Includes bibliographical references
We present a novel approach to training discriminative tree-structured machine trans- lation systems by learning to search. We describe three primary innovations in this work: a new parsing coordinator architecture and algorithms to generate the required training examples for the learning algorithm; a new semiring that provides an unbiased way to compare translations; and a new training objective that measures whether a translation inference improves the quality of a translation. We also apply the reinforcement learning concept of exploration to SMT. Finally, we empirically evaluate our innovations.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781267799647Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
Optimizing Machine Translation by Learning to Search.
LDR
:01709ntm a2200313K 4500
001
915388
005
20180727125214.5
006
m o u
007
cr mn||||a|a||
008
190606s2012 xx obm 000 0 eng d
020
$a
9781267799647
035
$a
(MiAaPQ)AAI3546405
035
$a
(MiAaPQ)nyu:11300
035
$a
AAI3546405
040
$a
MiAaPQ
$b
eng
$c
MiAaPQ
100
1
$a
Galron, Daniel.
$3
1188722
245
1 0
$a
Optimizing Machine Translation by Learning to Search.
264
0
$c
2012
300
$a
1 online resource (218 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.
500
$a
Adviser: Dan Melamed.
502
$a
Thesis (Ph.D.)--New York University, 2012.
504
$a
Includes bibliographical references
520
$a
We present a novel approach to training discriminative tree-structured machine trans- lation systems by learning to search. We describe three primary innovations in this work: a new parsing coordinator architecture and algorithms to generate the required training examples for the learning algorithm; a new semiring that provides an unbiased way to compare translations; and a new training objective that measures whether a translation inference improves the quality of a translation. We also apply the reinforcement learning concept of exploration to SMT. Finally, we empirically evaluate our innovations.
533
$a
Electronic reproduction.
$b
Ann Arbor, Mich. :
$c
ProQuest,
$d
2018
538
$a
Mode of access: World Wide Web
650
4
$a
Computer science.
$3
573171
655
7
$a
Electronic books.
$2
local
$3
554714
690
$a
0984
710
2
$a
ProQuest Information and Learning Co.
$3
1178819
710
2
$a
New York University.
$b
Computer Science.
$3
1184382
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3546405
$z
click for full text (PQDT)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login