System Combination of
RBMT plus SPE andPreordering plus SMT	
Terumasa EHARA	
Ehara NLP Research Laboratory	
http://www.ne.jp/asahi/eharate/eharate/		
2015. 10. 16	
WAT2015	

Introduction	
• Hybrid system combining rule-based
method (RBMT, preordering) andstatistical method (SMT, SPE) may
make MT more accurate.	
• Several improvements areimplemented.		

2	

System architecture	
Input
RBMT

RBMT

Preordering

Semi-­‐monotone Semi-­‐monotone
SPESMTCandidate
selection
Output3	

RBMT part	U serSentence
TaskEngine
dictionary pattern	 dic.
En-jaC om m ercial--JP O 	 dicZh-ja
C om m ercial-1,463,265
JP O 	 dicJP O zh-ja C om m ercial
131,463,265
JP O ko-ja C om m ercial 434,334--

4	SPE part	• Phrase based Moses	
• LM for en-ja and zh-ja:
3.6M sent., KenLM, order 6	
• LM for JPOzh-ja and JPOko-ja:
5M sent. (with NTCIR-10 data),
KenLM, order 6	
• TM size: see later	• Distortion limit: 6 (semi-monotone)		

5	

Preordering part (1)	• En-ja, zh-ja, JPOzh-ja task only	• Stanford segmenter	• Berkeley parser	
• Rule-based preordering	
VP	  ⇒ ADVP	  VBN	  PP	  2	  0	  1	  	  (widely	  u3lized	  in	  many	  ﬁelds)	  	  VP ⇒ VV	  NP	  IP	  1	  2	  0	  (使 各个 电动机 13	  旋转 驱动)	6	

Preordering part (2)	• Improvements of the parser	
üChinese	  grammar	  improvement 	ü	  Reranking	  of	  k-­‐best	  parse	  trees	7	Chinese grammar improvement (1)	• Determine gold standard of Chinese
sentence head	
シンチレータ ５４ は 、 Ｘ線 を 受けて 光 を 発生 する 。			

闪烁器 54 接收 X 射线 产生 光 。 	  	• Select best parse tree from 100 parsetrees	8	Chinese grammar improvement (2)	• Select the best parse tree ⇒ (b)	┗IP
┣NP┃┣NR━闪烁器
┃┗NN━54┣VP┃┣VV━接收┃┣NP┃┃┣NN━X┃┃┗NN━射线┃┗IP┃ ┗VP┃ ┣VV━产生┃ ┗NP┃ ┗NN━光┗PU━。

┗IP┣NP┃┣NR━闪烁器
┃┗NN━54┣VP┃┣VP┃┃┣VV━接收┃┃┗NP┃┃ ┣NN━X┃┃ ┗NN━射线┃┗VP┃ ┣VV━产生┃ ┗NP┃ ┗NN━光┗PU━。

(a)	

(b)	

• Retraining the grammar	
9	

Chinese grammar improvement (3)	• Sentence head agreement rate withthe gold standard (devtest data)	
G ram m arA greem ent	 rate	 (%)
O riginal
50.5(chn_sm 5.gr)
Im proved63.0(chn_jpo.gr)

10	Reranking of k-best parse trees (1)	• Reranking with the reordering accuracy(WER, for training data)
Gold:闪烁器

54

P(a):闪烁器

54

P(b):闪烁器

54

XX射线
射线

X 射线

接收光光产生接收光产生。

接收。

产生。	

11	

Reranking of k-best parse trees (2)	• Reranking with the LM score for dev,devtest and test data
LM: trained by the reordered training data(1M)
LM score = output of query1 / # of words
1. ~/mosesdecoder/bin/query	
12	

SMT part	• Phrase based Moses	• LM: same as SPE’s LM	
• TM:	 TaskC orpus	 size
JP C ko-ja
JP C zh-ja
zh-ja
en-ja

994,998
995,385
668,468
2,351,575• Distortion limit: 6 (semi-monotone SMT)	
13	

Candidate selection part	• Selection with the LM score
Number of selected candidates	
Task
JP C ko-ja
JP C zh-ja
zh-ja
en-ja

P reordering
RBM T
R B M T+SP ETotal
+SM T
25
1177
798
2000
2875
1123
2000
91270
828
2107
5658
1149
1812
14	

JPOzh-ja results for devtest data	S ystem
RBM T
RBM T
R B M T +S P E
SM T
P reordering+S M TP reordering+S M T
P reordering+S M T
S ystem 	 c om bination

A dditional	 feature
U ser	 dictionary
U ser	 dictionary(D L=10)original	 gram m ar
im proved	 gram m ar
im proved	 gram m ar	 +reranking	 o f	 parsetrees

B LEU

R IB ES
16.55
0.719223.54
0.751041.35
0.820340.86
0.807140.15
0.816441.84
0.821842.75

0.823743.01

0.826515	

Conclusion and future works	
• Combination of rule-based method andstatistical method.	
• Human evaluation results:	
Task
C row d JP O 	 adequacy
En-ja
Zh-jaJP O zh-ja
JP O ko-ja

11/15
2/11
3/12
4/13

-3/3
2/3
--	• To improve parsing accuracy.	
16