Improving Japanese-to-English Neural MachineTranslation by Voice Prediction
Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato and Mamoru Komachi
Tokyo Metropolitan University
Our previous study [Yamagishi+ Workshop on Asian Translation 2016] presented a voice controlling method to generate sentences
for NMT. We demonstrated that the BLEU score improved when the voice of generated sentence was controlled relative to that ofAbstract
the reference. However, it is impractical to use the reference information because we cannot discern the voice of the correcttranslation in advance. Thus, this study presents a voice prediction method for generated sentences for NMT.

Proposed Method
Voice Controlling Method [Yamagishi+ WAT2016]

Statistics of voice in ASPEC
Sentences (Train data)
Japanese side
English side

[Train Target] The bone density is explained .
① Parsing the English sentences.
② Is there “be-verb + past participle”
structure in the main clause?
Yes → <Passive> label
No → <Active> label

Active992,902604,158

Total
1,103,336
1,103,336

The NMT model should classify which voice is appropriate.
④ Training an NMT
model with labeled
Japanese sentences.

③ Adding each label into the end ofJapanese sentences as a word.
[Train Source] 骨 密度 に ついて 解説 し た 。 <Passive>⑤ Adding the specified voice labels into the end of test sentences.
[Test Source] 骨 密度 に ついて 解説 し た 。 <Active>⑥ Test and check[Test Target] We explain the bone density.

Voice Prediction
Developing a voice classifier using the following features.
Some of these features are selected and concatenated as a vector
used to train a logistic regression model.
(The features marked * are used on the only ASPEC experiments.)
1. Subject in a source sentence.
2. Predicate in a source sentence.
3. Predicate in the previous source sentence.*
4. Voice of the source sentence.
5. Objects in the previous target sentence.*
6. Voice of the output sentences from previous three sentences.*
7. The majority of target voice of each predicate in a sourcesentence.
ASPEC: Obtaining 67.7% accuracy using feature 2, 6 and 7.
NTCIR: Obtaining 66.0% accuracy using feature 1, 2 and 4.

Experiments
Baseline: Training and testing with the non-labeling data.
Labeling patterns
ALL_ACTIVE: All sentences to active voice.
ALL_PASSIVE: All sentences to passive voice.
REFERENCE: Each sentence to the same voice as that of thereference sentences.
PREDICT: Each sentence to the predicted voice.
Test data: 100 active sentences (not include the intransitive verb)
and 100 passive sentences extracted from the official test data.
BLEU score was calculated with this 200 sentences.
Tow comparisons:
Voice Accuracy: The voice of output and that of the reference.
Control Accuracy: The voice of the output and the added label.

Settings
Corpus: (The English sentences were written by the non-natives.)
ASPEC (Abstracts of scientific papers)• 1,103,336 sentences (329,025 documents)
NTCIR PatentMT Parallel Corpus• 1,169,201 sentences
Model: NMT with attention [Bahdanau+ ICLR2015]• Vocabulary: 30,000 (word-level)• Dimensions of the embedding and hidden unit: 512
Experiment

Passive
110,434
499,178

Examples of single sentence

Results
Experiment(ASPEC)
Test data
Baseline
ALL_ACTIVE
ALL_PASSIVE

ActivePassive

100
31147
6100
163
44189

REFERENCE
PREDICT8274113118

Experiment(NTCIR)
Test data
Baseline
ALL_ACTIVE
ALL_PASSIVE

ActivePassive

10069127
17100127
69186

REFERENCE
PREDICT

80
73116
122

Error

Voice
Accuracy

Control
Accuracy

BLEU

69560.5%
57.5%
51.0%

73.5%
94.5%

20.60
20.22
20.18

5889.5%
64.0%
89.0%

22.47
21.06

Error

Voice
Accuracy

Control
Accuracy

BLEU

34
3

66.0%
71.0%
55.0%

63.5%
93.0%

31.80
31.91
32.32

4
5

89.5%
69.5%
83.0%

33.90
33.16

Discussion
1.
2.
3.
4.

It is easy to generate the passive sentences.
The voice information of the previous sentences are good feature.
This method is useful for the single sentences.
The voice of coordinate clauses tend to become the same as thatof main clause.
5. The voice of subordinate clauses tend to be active.
Examples of complex sentence

Sourceリサイクルに関する最近の話題を紹介した。また、ドットの形状及び結晶性は温度に依存することもわかった。
Reference
Recent topics on recycling are introduced.
It was also proven that the shape and crystallinity of the dots were dependent on temperatures.
To be Active This paper introduces recent topics on recycling. The morphology and the crystallinity of the dots depended on the temperature.
To be Passive Recent topics on recycling are presented.
It was also found that the shape and crystallinity of the dots depend on the temperatures.