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15/07/29 1
Syntax-based Simultaneous Translation
through
Prediction of Unseen Syntactic Constituents
Yusuke Oda
Graham Neu...
15/07/29 2
Two Features of This Study
● Syntax-based Machine Translation
– State-of-the-art SMT method for distant languag...
15/07/29 3
Delay
(depends on the input length)
Speech Translation - Standard Setting
in the next 18 minutes
I 'm going to ...
15/07/29 4
Shorter delay
Simultaneous Translation with Segmentation
● Separate the input
at good positions
ASR
SS
今から
18分で...
15/07/29 5
Unseen VP
Syntactic Problems in Segmentation
● Segmentation allows us to translate each part separately
● But o...
15/07/29 6
Motivation of This Study
● Predict unseen syntax constituents
In the next 18 minutes I
PP
NPIN
NP
NN
NP
NNSCDJJ...
15/07/29 7
Summaries of Proposed Methods
● Proposed 1: Predicting and using unseen constituents
● Proposed 2: Waiting for ...
15/07/29 8
What is Required?
● To use predicted constituents in translation, we need:
1. Making training data
2. Deciding ...
15/07/29 9
Leaf span
Making Training Data for Syntax Prediction
● Decompose gold trees in the treebank
S
VPNP
NN
NP
DT
VBZ...
15/07/29 10
VP ... 0.65
NP ... 0.28
nil ... 0.04
...
Syntax Prediction from Incorrect Trees
Iminutes18nextthein
PP
NPIN
NP...
15/07/29 11
Syntax-based MT with Additional Constituents
● Use tree-to-string (T2S) MT framework
This is NP
This is
DT VBZ...
15/07/29 12
you on a journey 旅の途中で
Translation Waiting (1)
● Reordering problem
– Right syntax sometimes goes left in the ...
15/07/29 13
Waiting for Translation
● Heuristics: waiting for the next input
in the next 18 minutes I (VP) 今から18分で私は(VP)
'...
15/07/29 14
Experimental Settings
● Dataset Prediction: Penn Treebank MT: TED [WIT3]
● Languages English → Japanese
● Toke...
15/07/29 15
Results: Prediction Accuracies
● Half precision
– Not trivial problem
● Low recall
– Caused by redundant const...
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
Translati...
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
Translati...
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
Translati...
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
Tra...
15/07/29 20
Summaries
● Combining two frameworks
– Syntax-based machine translation
– Simultaneous translation
● Methods
–...
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Syntax-based Simultaneous Translation through Prediction of Unseen Syntactic Constituents

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Syntax-based Simultaneous Translation through Prediction of Unseen Syntactic Constituents

  1. 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. 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 これ は (NP) で す 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. 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 今から18分で 皆様を旅にお連れします Machine Translation Speech Synthesis ● Problem: long delay (if few explicit sentence boundaries)
  4. 4. 15/07/29 4 Shorter delay Simultaneous Translation with Segmentation ● Separate the input at good positions ASR SS 今から 18分で 皆様を お連れします 旅に 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. 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. 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 今 から 18 分 私 今 から 18 分 で 私 は (VP)
  7. 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 これは NP です これはペンですthis is a pen Waiting this is Proposed 2 Proposed 1 SyntaxPrediction ASR Segmentation Translation Parsing Output
  8. 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. 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. 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. 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 これ は NP で す Parse MT – Obtains state-of-the-art results on syntactically distant language pairs (e.g. English→Japanese) – Possible to use additional syntactic constituents explicitly
  12. 12. 15/07/29 12 you on a journey 旅の途中で Translation Waiting (1) ● Reordering problem – Right syntax sometimes goes left in the translation in the next 18 minutes I (VP) 今から18分で私は(VP) 'm going to take (NP) (NP)を行っています Reordering – Considering the output language, we should output future inputs before current input
  13. 13. 15/07/29 13 Waiting for Translation ● Heuristics: waiting for the next input in the next 18 minutes I (VP) 今から18分で私は(VP) 'm going to take (NP) (NP)を行っています ● Expect to avoid syntactically strange segmentation Wait 'm going to take you on a journey 貴方を旅にお連れします
  14. 14. 15/07/29 14 Experimental Settings ● Dataset Prediction: Penn Treebank MT: TED [WIT3] ● Languages English → Japanese ● Tokenization Stanford Tokenizer, KyTea ● Parsing Ckylark (Berkeley PCFG-LA) ● MT Decoder Moses (PBMT), Travatar (T2S) ● Evaluation BLEU, RIBES Methods Summary Baselines PBMT PBMT (Moses) ... conventional setting T2S T2S-MT (Travatar) without constituent prediction Proposed T2S-MT (Travatar) with constituent prediction & waiting
  15. 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. 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. 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. 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. 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. 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 これは NP です これはペンですthis is a pen Waiting this is SyntaxPrediction Translation Parsing Output

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