1. No category

LIMSI @WMT13
Alexandre Allauzen, Nicolas Pécheux, Quoc Khanh Do, Marco Dinarelli, Thomas Lavergne, Aurélien
Max, Hai-Son Le, François Yvon Univ. Paris Sud and LIMSI—CNRS Orsay, France
n-code
Highlights
n-code models
Tuples are bilingual units
!
{French,German,Spanish}↔English shared translation tasks
• n-code (http://ncode.limsi.fr):
org : ....
– Source reordering and n-gram translation models (TMs)
– n-best reranking with SOUL LM and TM
• First participation to the Spanish-English task
S : ....
T : ....
• Preliminary experiments with source pre-ordering
• Tighter integration of continuous space language models
à
recevoir
le
s̅8: à
s̅9: recevoir
s̅10: le
prix nobel de la paix
s̅11: nobel de la paix
s̅12: prix
̅t8: to
̅t9: receive
̅t10: the
̅t11: nobel peace
̅t12: prize
u8
u9
u10
u11
u12
The translation model is a n-gram model of tuples
• 3-gram tuple LM and 4-gram target word LM
• Four lexicon models (similar to the phrase table)
• Two lexicalized reordering models
(orientation of next/previous translation unit)
• Weak distance-based distortion model
• Word-bonus and a tuple-bonus
• Different tuning sets according to the original language
Data Processing
Artificial Text generation with SOUL
• Better normalization tools provide better BLEU scores
Issue:
• Specific pre-processing for German as source language
• Conventional models are used to build the pruned search space and
the potentially sub-optimal k-best lists
1
• Using lemmas and POS for the Spanish-English (freeling )
• Cleaning noisy data sets (CommonCrawl )
– Filter out sentences in other languages
• Continuous space models can only be used to rerank k-best lists
ppx
• Language model tuning
A solution for the language model:
• Sample text from a 10-gram NNLM
• Estimate a conventional 4-gram model used by the decoder.
– Sentence alignment
– Sentence pair selection using a perplexity criterion
1:
http://nlp.lsi.upc.edu/freeling/
Pre-ordering (English to German)
Experiments:
• ITG-like parser to
generate
reordered
source sentences
• On the English to German task
Tuning vs original language
• Different tunings for different original languages:
• Only 16% of the
source training sentences are modified
• No clear impact
Histogram of token movement size
1
:http://www.phontron.com/lader/
Direction
Baseline fr2en
22.3
36.4
31.6
18.5
30.2
29.4
6
8
10
12
Experimental Results
en2de
Solution:
Original language
cz
en
fr
de
es
all
4
times 400M sampled sentences
• The original language of a text has a significant impact on translation performance
• Build a SMT system
using pre-ordered parallel data
artificial texts
artificial+original texts
original texts
2
Assumption:
1
280
270
260
250
240
230
220
210
200
190
Tuning per source
23.8
39.2
32.4
18.5
29.3
30.1
de2en
en2fr
fr2en
en2es
es2en
System
base
+artificial text
+SOUL
+artificial text+SOUL
base+SOUL
base+SOUL
base
tuning per source
+SOUL
base+pos
base+pos+lem
BLEU
dev nt09 test
15.3
15.5
16.4
16.5
22.8
29.3
29.1
30.1
dev nt11
32.3
30.7
nt10
16.5
16.8
17.6
17.8
24.7
32.6
29.4
30.1
30.6
test nt12
33.8
33.9