Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation
Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato and Mamoru Komachi

Tokyo Metropolitan UniversityAbstract
We must consider the difference in expression between languages in MT. For example, the active/passive voice may change in Japanese-English translation. MT systems should consider the information structure to improvethe coherence of the output. Sennrich et al. (NAACL, 2016) attempted to control the honorific in English-German NMT. Similar to Sennrich et al., we report on our attempt to control the voice of a sentence generated by anencoder-decoder.
Verb in the training data
# Activeshow
21,703
describe
12,300introduce
6,030
examine3,795
detect468# Passive
11,441
17,414
9,167
11,1002,858# Total32,14429,774
15,197
14,895
3,326

Automatic Labeling of the Voice① Recognizing the voice of the target (English) sentence.
② Adding a special token, <Active> or <Passive>, as a word to the endof the source sentence.
The design and characters of the circuit were explained.
① Is the root the verb in
the past participle form?
ANDIs there a be-verb in thechildren of the root?
Yes → <Passive>
No → <Active>② Training an attentional
encoder-decoder model[Bahdanau+ 2015] with alabeled Japanese sentence.

回路 の 設計 と 特性 を 解説 し た 。 <Passive>[Test] 回路 の 設計 と 特性 を 解説 し た 。 <Active>
Positive result
We explained the design … .

Negative result
The design of … were explained.

Settings
Corpus: ASPEC (Asian Scientific Paper Expert Corpus)• 827,503 sentences, obtained by eliminating sentences with more than
40 words in the first 1 million sentences.
• Word2Vec (gensim) was trained with all 3.0M sentences of ASPEC.
Tools• MeCab (the tool of Japanese Morphological Analysis)• Cabocha (the tool of Japanese Dependency Structure Analysis)• Both of them used the Dictionary IPADIC ver. 2.7.0• Stanford Parser 3.5.2
Hyper-Parameters of Encoder-Decoder• Vocabulary: 30000, epoch: 10• Embed size and Hidden size: 512, Batch size: 128• Optimizer: Adagrad (Learning rate: 0.01)

Experiments① Train the attentional encoder-decoder by the labelled data.
② Add the label to the end of sentence of the test data.
③ Check the voice of output sentence.
Testing the following four patterns of labeling the voice features.
a. ALL_ACTIVE: Controlling all target sentences to the active voice.
b. ALL_PASSIVE: Controlling all target sentences to the passive voice.
c. REFERENCE: Controlling each target sentence to the same voice asthat of the reference sentence.
d. PREDICT: Controlling each target sentence to the predicted voice.
• Adding the majority label of the voice distribution in the training set.
• It was submitted to WAT 2016
We checked the voice of 200 generated sentence manually.
We calculated the BLEU score with the test data of all 1812 sentences.
The accuracy was calculated as the agreement between the label and thevoice of the generated sentence.
回路 の 設計 と 特性 を 解説 し た 。
Is the root in the voice Distribution DB of
the training data?
Yes → Choosing the majority of
the Voice Distribution DB.
No → <Active>

How to PREDICT (d.)

Voice Distribution DB…
解説した (explained)<Active>: 88, <Passive>: 117…

[PREDICT] 回路 の 設計 と 特性 を 解説 し た 。 <Passive>
Positive examples
InputReferenceACTIVEPASSIVE

Result of Experiments Active Passive Error Accuracy BLEU
Reference
1001000Baseline (No Labels)
74117
9- 20.53
a. ALL_ACTIVE
151
361375.5% 19.63
b. ALL_PASSIVE
17175
887.5% 19.93
c. REFERENCE
97
94
989.5% 21.26
d. PREDICT(Compared to Ref.)
72
121
769.5% 20.42(Compared to Label)
87.5%Discussion
1. There were many sentences that persisted the “be-verb + verb
in past participle form” structure.
2. In the case that the root verb in the target should be an
intransitive verb, it exchanged like ”do ⇄ be found to do”
or ”can be done ⇄ is able to be done”.
3. The result of voice controlling tended to fail sometimes if weinput the verb that had the skewed voice distribution.
4. PREDICT failed to predict the voice of the reference,
especially with high-frequency verbs.

Future work
• PREDICT resulted in decrease in the BLEU, so we want tothink about another method how to predict.
• We will study how the non-root verb must be treated in orderto obtain the consistency of the document expression.

[P1] The voice of reference is ”Active.”
熱戻り反応の機構を議論した。
This paper discusses the mechanism of the heat return reaction.
We discuss the mechanism of the thermal return reaction.
The mechanism of the thermal return reaction is discussed.

[P2] The voice of reference is “Passive.”
リサイクルに関する最近の話題を紹介した。
Recent topics on recycling are introduced.
This paper introduces recent topics on recycling.
Recent topics on recycling are introduced.

Negative examples [N1] The voice of the target is controlled, but meaning is different. [N2] The voice of the target is not controlled.
Input
自己組織化構造に分子の形と分子間相互作用が大きく影響する。 その結果、THZ波はSTJでのトンネリング電流信号として検出できる。
Reference
Molecular shape and intermolecular interaction influence selfConsequently, the THZ waves can be detected asassembled structures greatly.
tunneling current signals at STJ.
ACTIVEThe molecular structure and molecular interaction greatly affect the As a result, the THZ wave can be detected as a current
self-organization structure.
current signals in the <unk>.
PASSIVE
The molecular structure and molecular interaction are greatly
As a result, the THZ wave can be detected as a current
affected by the self-organization structure.
current signals in the <unk>.