nudt machine translation system for iwslt2007
DESCRIPTION
NUDT Machine Translation System for IWSLT2007. Presenter: Boxing Chen Authors: Wen-Han Chao & Zhou-Jun Li National University of Defense Technology, China. Outline. System Description Word Alignment Phrase Pair Extracting Example-Based Decoder Experiments Future Works. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/1.jpg)
NUDT Machine Translation System for IWSLT2007
Presenter: Boxing Chen
Authors: Wen-Han Chao & Zhou-Jun Li
National University of Defense Technology, China
![Page 2: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/2.jpg)
Outline
System Description Word Alignment Phrase Pair Extracting Example-Based Decoder
Experiments Future Works
![Page 3: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/3.jpg)
System Description
Three Main Components Word Alignment Phrase Pair Extraction Example-based Decoder
![Page 4: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/4.jpg)
Word Alignment - (1) DP algorithm and log-linear model to co
mpute the word alignment Feature functions
ITG constraint Word co-occurrence probability Distortion model
Word alignment a binary branching tree satisfy ITG constraint many-to-many alignment
![Page 5: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/5.jpg)
Word Alignment - (2) 我 再次 检查 我 的 包
I checked my bag once again
(b) An ITG tree for the word alignment (a)
我/ I 再次/once again
我 的 / my
包/ bag
检查/ checked
(a) A valid word alignment example
![Page 6: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/6.jpg)
Phrase Pair Extraction Extracting phrase pairs
each constituent sub-tree is a phrase pair (block)
Building models Direct and inverse word-
and phrase-based TM Reordering model over
all block pairs
Chinese English
我 i
再次 once again
检查 checked
我 的 my
包 Bag
我的 包 my bag
检查 我的 包 checked my bag
再次 检查 我的 包
checked my bag once again
我 再次 检查 我的 包
i checked my bag once again
![Page 7: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/7.jpg)
Decoding Log-linear model used to score the
partial hypotheses Baseline: CKY style decoder Example-based decoder
Examples retrieval Generation: matching and merging
![Page 8: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/8.jpg)
Example-based Decoder - (1) Examples retrieval
Collect source blocks from input Collect (top N) phrase pairs from the
TM for each source block If an example (sentence-pair) can be
matched by at least one phrase-pair, it is a valid example.
Keep top M examples for a specific phrase-pair
![Page 9: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/9.jpg)
Example-based Decoder - (2)
Matching Matching the input with each example
get a translation template the translation template forms a new ITG
tree. Decoding each un-translated string (child
input) iteratively get a set of child translation templates.
![Page 10: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/10.jpg)
(b) Example A
你 能 拼 一下 吗 ?
could you spell it ? 拼 / spell 能/ could 吗?/ ? 一下/ε ε / it 你/you
你 能 打开 你的 吗 ? 包
(a) Input
Example-based Decoder - (3)
![Page 11: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/11.jpg)
(c) Translation Tempate after match input with Example A
你 能 打开 你的 吗 ?
could you ? 打开 你的 包 能/ could 吗?/ ? 你/you
包
Example-based Decoder - (4)
![Page 12: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/12.jpg)
(d) Example B
请
开
打开 你的 包 。
please open your bag .. 你的 / your 打开/ open 。/ . 包/bag 请/please
(e) Translation Tempate after match the child input with Example B
你的 / your 打开/ open 包/bag
打开 你的 包
open your bag
Example-based Decoder - (5)
![Page 13: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/13.jpg)
Example-based Decoder - (6)
Merging merging the child templates with the
parent templates. consider the child template as a sub-tree merge it with the parent ITG tree.
![Page 14: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/14.jpg)
(f) Final translation after merged (c) and (e)
吗 ?
?
你 能
could you
打开 你的 包
open your bag 能/ could 吗?/ ? 你/you 你的 / your 打开/ open 包/bag
Example-based Decoder - (7)
![Page 15: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/15.jpg)
Example-based Decoder - (8)
beam search scoring function for each translation
template is:
)(log)|(log)( untranstranstrans CHCEPtempf
![Page 16: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/16.jpg)
Experiments-(1) We take part in the C-E task
Training corpus 39,953 sentence pairs
development set IWSLT07_devset3_* 506 Chinese sentences and 16 references
test set IWSLT07_devset2_* 500 Chinese sentences and 16 references
official test set 489 Chinese sentences
![Page 17: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/17.jpg)
Experiments-(2) Preprocessing
tokenization lowercasing Stemming by
using a morphological dictionary
Chinese English (stemme
d)
Train.
Data
Sentences 39,963
Words 351,060 377,890
Vocabulary
11,302 7,610
Dev.Set
Sentences 506
Words 3,826
TestSet
Sentences 489
Words 3,189
![Page 18: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/18.jpg)
Experiments-(3) Baseline
CKY decoder
Results for the stemmed corpus
Decoder
Bleu
Dev set
TestSet
OfficialTest set
CKY-Decode
r
0.3614 0.3156 0.2741
EB-Decode
r
0.3958 0.3334 0.3012
![Page 19: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/19.jpg)
Experiments-(4) Post-Processing
Morphological changes using a morphological dictionary and 3-
gram LM Case sensitive outputs: two simple rules
Uppercasing the first word of a sentence Changing the word “i” to “I”
![Page 20: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/20.jpg)
Experiments-(5) Reasons for the result
the data becomes more sparse the post-processing is too simple
Decoder
Bleu
Dev set
Test set
Official Test set
CKY-Decoder
0.1843
0.1769
0.1758
EB-Decoder 0.2024
0.1966
0.1934
![Page 21: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/21.jpg)
Future Works Better examples retrieval algorithm. More feature functions in log-linear model. Improving morphological generation.
![Page 22: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/22.jpg)
Thank Dr. Boxing Chen for the presentation and the useful suggestions
Thank you for your attention. Any Questions?
![Page 23: NUDT Machine Translation System for IWSLT2007](https://reader035.vdocuments.us/reader035/viewer/2022062410/56815809550346895dc57832/html5/thumbnails/23.jpg)
Main References Philipp Koehn, Franz Josef Och and Daniel Marcu: “Statistical Phrase-Based Translation”. In NAACL/
HLT 2003, pages 127-133(2003) David Chiang: “A Hierarchical Phrase-Based Model for Statistical Machine Translation”. In Proc. of A
CL 2005, pages 263–270 (2005) Franz Joseph Och and Hermann Ney: “Discriminative training and maximum entropy models for stati
stical machine translation”. In Proceedings of the 40th Annual Meeting of the ACL, pp. 295–302(2002) Dekai Wu: “Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora”.
Computational Linguistics, 23(3):374(1997) Franz Joseph Och and Hermann Ney: “A Systematic Comparison of Various Statistical Alignment Mod
els”. Computational Linguistics, 29(1):19–52, March(2003) Wen-Han Chao and Zhou-Jun Li: “Incorporating Constituent Structure Constraint into
Discriminative Word Alignment”, MT Summit XI, Copenhagen, Denmark, September 10-14, 2007, accepted. (2007)
R. Moore. “A discriminative framework for bilingual word alignment”. In Proceedings of HLT-EMNLP, pages 81–88, Vancouver, Canada, October. (2005)
I. Dan Melamed. “Models of Translational Equivalence among Words”. Computational Linguistics, 26(2):221–249. (2000)
Taro Watanabe and Eiichiro Sumita: “Example-based Decoding for Statistical Machine Translation”. In Machine Translation Summit IX pp. 410-417 (2003)
Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu: “BLEU: a Method for Automatic Evaluation of Machine Translation”. In Proceedings of the 40th Annual Meeting of the Association fo Computational Linguistics(ACL), Philadelphia, July 2002, pp. 311-318(2002)
A. Stolcke, “SRILM – An extensible language modeling toolkit,” in Proceedings of the International Conference on Spoken Language Processing, Denver, Colorado, 2002, pp. 901–904