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**Corpus Augmentation for Neural Machine Translation**  
**with Chinese-Japanese Parallel Corpora**  
**[PERSON] 1,* and [PERSON] 2**  
1 Electronics and Information Systems Engineering Division, Graduate School of Engineering, Gifu University, Gifu 501-1193, Japan  
2 Department of Electrical, Electronic and Computer Engineering, Gifu University, Gifu 501-1193, Japan;  
[EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 2 April 2019; Accepted: 9 May 2019; Published: 17 May 2019  
**Featured Application: Machine translation is a subfield of artificial intelligence that investigates transformation of text in the source language into its equivalent in the target language. Neural Machine Translation (NMT) is a recently-proposed framework for translation application based on sequence-to-sequence models: a large neural network is used to translate the source language sequence into the target language sequence. After years of development, NMT has produced richer translation results than ever over various language pairs, becoming a new machine translation model with great potential. As described in this paper, we present a corpus augmentation method. The method has two variations: one is for all language pairs and the other for the Chinese-Japanese language pair. The method generates pseudo-parallel sentence pairs to extend the original parallel corpus. This paper describes the results obtained in the Japanese-Chinese and Chinese-Japanese translation with the ASPEC-JC (Asian Scientific Paper Excerpt Corpus, Japanese-Chinese) corpus, which substantially improved the translation performance. We also supply code that can reproduce our proposed method.**  
The translation quality of Neural Machine Translation (NMT) systems depends strongly on the training data size. Sufficient amounts of parallel data are, however, not available for many language pairs. This paper presents a corpus augmentation method, which has two variations: one is for all language pairs, and the other is for the Chinese-Japanese language pair. The method uses both source and target sentences of the existing parallel corpus and generates multiple pseudo-parallel sentence pairs from a long parallel sentence pair containing punctuation marks as follows: (1) split the sentence pair into parallel partial sentences; (2) back-translate the target partial sentences; and (3) replace each partial sentence in the source sentence with the back-translated target partial sentence to generate pseudo-source sentences. The word alignment information, which is used to determine the split points, is modified with \"shared Chinese character rates\" in segments of the sentence pairs. The experiment results of the Japanese-Chinese and Chinese-Japanese translation with ASPEC-JC (Asian Scientific Paper Excerpt Corpus, Japanese-Chinese) show that the method substantially improves translation performance. We also supply the code (see Supplementary Materials) that can reproduce our proposed method.  
**Keywords:** back translation; Chinese-Japanese translation; corpus augmentation; decoder; encoder; Japanese-Chinese translation; LSTM; neural machine translation; sentence segmentation