1. 15/07/29 1 Syntax-based Simultaneous Translation through
Prediction of Unseen Syntactic Constituents Yusuke Oda Graham
Neubig Sakriani Sakti Tomoki Toda Satoshi Nakamura ACL, July 27,
2015
2. 15/07/29 2 Two Features of This Study Syntax-based Machine
Translation State-of-the-art SMT method for distant language pairs
This is (NP) This is DT VBZ (NP) VP NP S (NP) Parse MT Simultaneous
Translation Prevent translation delay when translating continuous
speech In the next 18 minutes I'm going to take you on a journey.
Translate Translate Translate Split Split
3. 15/07/29 3 Delay (depends on the input length) Speech
Translation - Standard Setting in the next 18 minutes I 'm going to
take you on a journey Speech Recognition 18 Machine Translation
Speech Synthesis Problem: long delay (if few explicit sentence
boundaries)
4. 15/07/29 4 Shorter delay Simultaneous Translation with
Segmentation Separate the input at good positions ASR SS 18 MT
Segmentation I 'm going to take you in the next 18 minutes on a
journey The system can generate output w/o waiting for
end-of-speech Translation Quality Segmentation Frequency
Trade-off
5. 15/07/29 5 Unseen VP Syntactic Problems in Segmentation
Segmentation allows us to translate each part separately But often
breaks the syntax In the next 18 minutes I 'm going to ... PP NPIN
NP NN NP NNSCDJJDT Iminutes18nextthein Predicted Boundary PP S IN
NP PRP NP NNSCDJJDT Iminutes18nextthein (VP) VP Bad effect on
syntax-based machine translation
6. 15/07/29 6 Motivation of This Study Predict unseen syntax
constituents In the next 18 minutes I PP NPIN NP NN NP NNSCDJJDT
Iminutes18nextthein PP S IN NP PRP NP NNSCDJJDT Iminutes18nextthein
(VP) VP Predict VP Translate from correct tree 18 18 (VP)
7. 15/07/29 7 Summaries of Proposed Methods Proposed 1:
Predicting and using unseen constituents Proposed 2: Waiting for
translation this is NPthis is a pen this is a pen NP this is a pen
Waiting this is Proposed 2 Proposed 1 SyntaxPrediction ASR
Segmentation Translation Parsing Output
8. 15/07/29 8 What is Required? To use predicted constituents
in translation, we need: 1. Making training data 2. Deciding a
prediction strategy 3. Using results for translation
9. 15/07/29 9 Leaf span Making Training Data for Syntax
Prediction Decompose gold trees in the treebank S VPNP NN NP DT VBZ
penaisThis DT 1. Select any leaf span in the tree 2. Find the path
between leftmost/rightmost leaves 3. Delete the outside subtree NN
4. Replace inside subtrees with topmost phrase label 5. Finally we
obtain: nil is a NN nil Leaf spanLeft syntax Right syntax
10. 15/07/29 10 VP ... 0.65 NP ... 0.28 nil ... 0.04 ... Syntax
Prediction from Incorrect Trees Iminutes18nextthein PP NPIN NP NN
NP NNSCDJJDT 1. Parse the input as-is Input translation unit
Word:R1=I POS:R1=NN Word:R1-2=I,minutes POS:R1-2=NN,NNS ... ROOT=PP
ROOT-L=IN ROOT-R=NP ... 2. Extract features VP nil
11. 15/07/29 11 Syntax-based MT with Additional Constituents
Use tree-to-string (T2S) MT framework This is NP This is DT VBZ NP
VP NP S NP Parse MT Obtains state-of-the-art results on
syntactically distant language pairs (e.g. EnglishJapanese)
Possible to use additional syntactic constituents explicitly
12. 15/07/29 12 you on a journey Translation Waiting (1)
Reordering problem Right syntax sometimes goes left in the
translation in the next 18 minutes I (VP) 18(VP) 'm going to take
(NP) (NP) Reordering Considering the output language, we should
output future inputs before current input
13. 15/07/29 13 Waiting for Translation Heuristics: waiting for
the next input in the next 18 minutes I (VP) 18(VP) 'm going to
take (NP) (NP) Expect to avoid syntactically strange segmentation
Wait 'm going to take you on a journey
15. 15/07/29 15 Results: Prediction Accuracies Half precision
Not trivial problem Low recall Caused by redundant constituents in
the gold syntax I 'm a NN nilOur predictor I 'm a JJ NN PP nilGold
syntax E.g. "I 'm a" Precision = 1/1 NN NN Recall = 1/3 JJNN NN PP
Precision = 52.77% Recall = 34.87% Actual performance
16. 15/07/29 16 Results: Translation Trade-off (1) 0 2 4 6 8 10
12 14 16 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15
TranslationAccuracy BLEU RIBES Mean #words in inputs Delay Short
Long Short Long 0 2 4 6 8 10 12 14 16 0.42 0.44 0.46 0.48 0.5 0.52
0.54 0.56 0.58 0.6 PBMT Short inputs reduce translation accuracies
Using N-words segmentation (not-optimized)
17. 15/07/29 17 Results: Translation Trade-off (2) 0 2 4 6 8 10
12 14 16 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15
TranslationAccuracy BLEU RIBES Mean #words in inputs Delay T2S PBMT
0 2 4 6 8 10 12 14 16 0.42 0.44 0.46 0.48 0.5 0.52 0.54 0.56 0.58
0.6 Short Long Short Long Long phrase ... T2S > PBMT Short
phrase ... T2S < PBMT
18. 15/07/29 18 Results: Translation Trade-off (3) 0 2 4 6 8 10
12 14 16 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15
TranslationAccuracy BLEU RIBES Mean #words in inputs Delay T2S PBMT
Proposed 0 2 4 6 8 10 12 14 16 0.42 0.44 0.46 0.48 0.5 0.52 0.54
0.56 0.58 0.6 Short Long Short Long Prevent accuracy decreasing in
short phrases More robustness for reordering
19. 15/07/29 19 Results: Using Other Segmentation 0 2 4 6 8 10
12 14 16 18 0.42 0.44 0.46 0.48 0.5 0.52 0.54 0.56 0.58 0.6
TranslationAccuracy Mean #words in inputs Delay 0 2 4 6 8 10 12 14
16 18 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 Short Long Short
Long BLEU RIBES Using an optimized segmentation [Oda+2014] T2S PBMT
Proposed Segmentation overfitting But reordering is better than
others
20. 15/07/29 20 Summaries Combining two frameworks Syntax-based
machine translation Simultaneous translation Methods Unseen syntax
prediction Waiting for translation Experimental results Prevent
accuracy decrease in short phrases More robustness for reordering
Future works Improving prediction accuracies Using other context
features 0 2 4 6 8 10 12 14 16 0.42 0.44 0.46 0.48 0.5 0.52 0.54
0.56 0.58 0.6 this is NPthis is a pen NP this is a pen Waiting this
is SyntaxPrediction Translation Parsing Output
