Phrase-Based SMT with Shallow Tree-Phrases

Transcription

Phrase-Based SMT with Shallow Tree-Phrases
Philippe Langlais and Fabrizio Gotti
RALI
Université de Montréal, Canada
http://rali.iro.umontreal.ca
June 8th
P. Langlais and F. Gotti (RALI)
SMT with Shallow Tree-Phrases
June 8th
1 / 34
Context
Recently, many syntax-aware SMT systems have been proposed
(Chiang, 2005 ; Ding and Palmer, 2005 ; Quirk et al., 2005 ; Galley
and al., 2004 ; Lin, 2004 ; Melamed,2004 ; . . . )
Phrases with gaps (Simard et al., 2005)
Middle ground between (Quirk et al., 2005) and (Simard et al.,
2005) : the Tree-Phrase.


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














, the : acting : speaker
président
M




qqq MMMMM


q


q


M
q
q






le
suppléant
June 8th
2 / 34
1
Motivation
2
Training
3
Decoding
4
Experiments
5
Conclusions
June 8th
3 / 34
Syntex
Syntex (Bourigault and Fabre,2000) : a robust syntactic parser available
for French and English
Source sentence
Request for federal funding
Syntex output
NOUN?S|request|Request|1|0|PREP;2
PREP|for|for|2|PREP;1|NOUNPREP;4
ADJ|federal|federal|3|ADJ;4|0
NOUN?S|funding|funding|NOUNPREP;2|ADJ;3
June 8th
5 / 34
Syntex
We used Syntex to parse the French part of an Hansard bitext (≈ 1.7 M.
pairs of E-F sentences)
a demandé
YY
SUBlllll
l
llll
on
YYYYYYOBJ
YYYYYY
YYYY
crédits
R
DETlllll
l
llll
RRRRADJ
RRRR
R
des
fédéraux
On a demandé des crédits fédéraux
(Request for federal funding)
June 8th
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Building the Tree-Phrase Table
1
2
token alignment : a demandé ≡ request for, fédéraux ≡ federal,
crédits ≡ funding
treelets acquisition (by parsing the source material with Syntex)
crédits
M
a demandé
M
qM
qqq MMMMM
q
q
q
qM
qqq MMMMM
q
q
q
on
3
crédits
des
fédéraux
tree-phrases extraction (by simple token-alignment projection)
treelet?
elastic phrase?
{{on@-1} a demandé {crédits@2}}
|request@0| |for@1| |funding@3|
treelet
elastic phrase
{{des@-1} crédits {fédéraux@1}}
|federal@0| |funding@1|
June 8th
7 / 34
The Tree-Phrase Table
s
2
3
4
5
6
7
8
all
|treelet|
936 180
1 399 288
464 062
45 234
1 898
52
4
2 846 718
%-c
48.2%
39.2%
35.6%
21.1%
10.7%
7.7%
0.0%
41.3%
|elastic phrase|
3 857 234
2 588 553
621 742
48 509
1 925
52
4
7 118 019
%-c
55.9%
49.2%
39.7%
24.6%
14.4%
7.7%
0.0%
51.8%
June 8th
8 / 34
Example of a long Tree-Phrase
tl
{{par@-3} {un@-2} {excellent@-1} Chili {con@1}
{carne@2} {servi@3} {Léger@6}}
ep
|culmination@0| |a@2| |Chili@3| |con@4| |carne@5|
|feast@6| |provided@7| |Leger@11|
src
La soirée s’ est terminée en beauté par un excellent Chili con
carne servi par Mme Léger .
trg
The culmination was a Chili con carne feast provided by Ms .
Leger .
June 8th
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The 5 Most Frequent Tree-Phrases
freq
75 051
32 601
26 347
14 515
13 043
tl
ep
tl
ep
tl
ep
tl
ep
tl
ep
tree-phrase
{{monsieur@-2} {Le@-1} président}
|Mr@0| |.@1| |Speaker@2|
{{Le@-1} gouvernement}
|the@0| |Government@1|
{{de@-2} {les@-1} voix}
|Some@0| |Honourable@1| |Members@2|
{{Le@-1} ministre}
|the@0| |Minister@1|
{{Madame@-2} {la@-1} Présidente}
|Madam@0| |Speaker@1|
June 8th
10 / 34
1
Motivation
2
Training
3
Decoding
4
Experiments
5
Conclusions
June 8th
11 / 34
A Standard Phrase-Based decoder
TRG:
the black cat ||| le chat noir
NULL ||| a
again, ||| encore une fois
crossed the street ||| traverser la rue
SRC:
again ,
the black cat
crossed the street
June 8th
12 / 34
TRG:
le chat noir
NULL ||| a
SRC:
again ,
the black cat
crossed the street
June 8th
13 / 34
TRG:
le chat noir
a
NULL ||| a
SRC:
again ,
the black cat
crossed the street
June 8th
14 / 34
encore une fois
TRG:
le chat noir
a
NULL ||| a
SRC:
again ,
the black cat
crossed the street
June 8th
15 / 34
encore une fois
traversé la rue
TRG:
le chat noir
a
NULL ||| a
SRC:
again ,
the black cat
crossed the street
June 8th
16 / 34
encore une fois
NULL ||| a
TRG:
le chat noir
a
generation
traversé la rue
SRC:
again ,
the black cat
crossed the street
June 8th
17 / 34
A Tree-Phrase Based Decoder
mr le président ||| the president
président a_demandé hier ||| president requested yesterday
des crédits fédéraux ||| federal funding
hier matin ||| yesterday morning
{des@−1} crédits {fédéraux@1} ||| federal@0 funding@1
{mr@−2} {le@−1} président ||| the@0 president@1
TRG:
{président@−1} a_demandé {crédits@3} ||| president@0 requested@1 funding@3
SRC: mr le président a_demandé
hier matin
des crédits
fédéraux
June 8th
18 / 34
TRG: the president
hier matin
des crédits
fédéraux
June 8th
19 / 34
TRG: the president requested GAP
funding
hier matin
des crédits
fédéraux
June 8th
20 / 34
TRG: the president
requested federal funding
SRC: mr le président
a_demandé
hier matin
des crédits
fédéraux
June 8th
21 / 34
yesterday morning
TRG: the president
requested federal funding
a_demandé
hier matin
des crédits
fédéraux
June 8th
22 / 34
Embedded Components
Log-linear combination of 9 components :
Phrase-Phrase table : relative frequency, IBM-1 like score
Tree-Phrase table : relative frequency, IBM-1 like score
Language model (Kneser-Ney implementation in SRILM)
Distortion (adapted from pharaoh)
Word and Phrase penalty
Future cost (adapted from pharaoh)
Coefficients tuned by grid searching
June 8th
23 / 34
Updating the Language Model Score
h
request
U
on
a_demandé
u
h’
for
B
request
U
on
funding
U
des
crédits
S[3]
fédéraux
TL: {on@−1} a_demandé {crédits@2}
EP: request@0 for@1 funding@3
for federal
B
T
a_demandé
u
F
des
funding
T
crédits
S[5]
fédéraux
TL: {des@−1} crédits {fédéraux@1}
EP: federal@0 funding@1
June 8th
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Need for a “definition” of two compatible treelets
Either no token in common, or only one that must be the head in
one treelet, and a dependent in the other.
Compatible :
a demandé
M
crédits
M
qM
qqq MMMMM
qqq
on
qM
qqq MMMMM
qqq
crédits
des
fédéraux
Not compatible :
président
président
M
qM
qqq MMMMM
q
q
q
mr.
le
suppléant
June 8th
25 / 34
Some (other) heuristics embedded
Tree-Phrase and Phrase-Phrase units compete.
a_demandé
hier matin
des crédits
fédéraux
a_demandé
hier matin
des crédits
fédéraux
a_demandé hier matin ||| requested yesterday morning
June 8th
26 / 34
1
Motivation
2
Training
3
Decoding
4
Experiments
5
Conclusions
June 8th
27 / 34
Corpora (In-house Canadian Hansards)
sentences
e-toks
f-toks
e-toks/sent
f-toks/sent
e-types
f-types
e-hapax
f-hapax
train
1 699 592
27 717 389
30 425 066
16.3 (± 9.0)
17.9 (± 9.5)
164 255
210 085
68 506
90 747
dev
500
8 160
8 946
16.3
17.9
(± 9.1)
(± 9.5)
2 224
2 481
1 469
1 704
test
8 000
129 601
143 237
16.2 (± 9.1)
17.9 (± 9.4)
12 143
14 484
6 673
8 381
test set : 16 (disjoint) slices of 500 sentences each.
results averaged over the slices
June 8th
28 / 34
Results
MT engine
pp
tp 1
wer (%)
52.58 ± 1.2
52.23 ± 1.1
ser (%)
94.20 ± 0.8
93.98 ± 0.9
bleu (%)
30.43 ± 1.4
30.70 ± 1.2
Significant at the 95% probability level for bleu and ser and significant
at the 99% level for wer (Wilcoxon signed-rank paired-test)
June 8th
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Extending to depth-2 Tree-Phrases
a demandé
YY
YYYYYYOBJ
YYYYYY
YYYY
SUBlllll
l
llll
on
crédits
R
DETlllll
l
llll
RRRRADJ
RRRR
R
des
fédéraux
a demandé
elelelelelYYYYYYYYYYY
e
e
e
e
e
l
YYYYYY
e
e
l
e
Y
eeeee
lll
on
des
crédits
fédéraux
June 8th
30 / 34
Results
MT engine
pp
tp 1
tp 2
wer (%)
52.58 ± 1.2
52.23 ± 1.1
51.44 ± 1.2
ser (%)
94.20 ± 0.8
93.98 ± 0.9
92.55 ± 1.3
bleu (%)
30.43 ± 1.4
30.70 ± 1.2
31.07 ± 1.3
June 8th
31 / 34
1
Motivation
2
Training
3
Decoding
4
Experiments
5
Conclusions
June 8th
32 / 34
Conclusions & Future Work
A viable approach to ad hoc syntax-based SMT
As lazy as phrase-based SMT
Statistically significant bleu-like improvements
Can be seen as :
a variant of (Simard et al.,2005)
a simplification of (Quirk et al., 2005)
June 8th
33 / 34
Future Work
Easy things that we must investigate :
We do not use parsing when translating
We do not encode the word alignment in a Tree-Phrase
Why not depth-n Tree-Phrases ?
More thorough investigations :
We did not consider elastic “gaps” in this study
Unclear at this stage why we observe improvements
June 8th
34 / 34

Phrase-Based SMT with Shallow Tree-Phrases

Transcription

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