TY - GEN
T1 - Case-Sensitive Neural Machine Translation
AU - Shi, Xuewen
AU - Huang, Heyan
AU - Jian, Ping
AU - Tang, Yi Kun
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Even as an important lexical information for Latin languages, word case is often ignored in machine translation. According to observations, the translation performance drops significantly when we introduce case-sensitive evaluation metrics. In this paper, we introduce two types of case-sensitive neural machine translation (NMT) approaches to alleviate the above problems: i) adding case tokens into the decoding sequence, and ii) adopting case prediction to the conventional NMT. Our proposed approaches incorporate case information to the NMT decoder by jointly learning target word generation and word case prediction. We compare our approaches with multiple kinds of baselines including NMT with naive case-restoration methods and analyze the impacts of various setups on our approaches. Experimental results on three typical translation tasks (Zh-En, En-Fr, En-De) show that our proposed methods lead to the improvements up to 2.5, 1.0 and 0.5 in case-sensitive BLEU scores respectively. Further analyses also illustrate the inherent reasons why our approaches lead to different improvements on different translation tasks.
AB - Even as an important lexical information for Latin languages, word case is often ignored in machine translation. According to observations, the translation performance drops significantly when we introduce case-sensitive evaluation metrics. In this paper, we introduce two types of case-sensitive neural machine translation (NMT) approaches to alleviate the above problems: i) adding case tokens into the decoding sequence, and ii) adopting case prediction to the conventional NMT. Our proposed approaches incorporate case information to the NMT decoder by jointly learning target word generation and word case prediction. We compare our approaches with multiple kinds of baselines including NMT with naive case-restoration methods and analyze the impacts of various setups on our approaches. Experimental results on three typical translation tasks (Zh-En, En-Fr, En-De) show that our proposed methods lead to the improvements up to 2.5, 1.0 and 0.5 in case-sensitive BLEU scores respectively. Further analyses also illustrate the inherent reasons why our approaches lead to different improvements on different translation tasks.
KW - Case-sensitive
KW - Natural language processing
KW - Neural machine translation
UR - http://www.scopus.com/inward/record.url?scp=85085732838&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-47426-3_51
DO - 10.1007/978-3-030-47426-3_51
M3 - Conference contribution
AN - SCOPUS:85085732838
SN - 9783030474256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 662
EP - 674
BT - Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
A2 - Lauw, Hady W.
A2 - Lim, Ee-Peng
A2 - Wong, Raymond Chi-Wing
A2 - Ntoulas, Alexandros
A2 - Ng, See-Kiong
A2 - Pan, Sinno Jialin
PB - Springer
T2 - 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
Y2 - 11 May 2020 through 14 May 2020
ER -