TY - GEN
T1 - A Comparative Study of Different Models in Ancient Poetry Translation
AU - Boyuan, Wang
AU - Xiangli, Le
AU - Hainan, Wang
AU - Baochang, Zhang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - Ancient poetry is an important part of Chinese culture. There have been projects like Jiuge to combine ancient poetry with deep learning. The language of ancient poetry is often refined, and it needs rich imagination to understand its meaning. As a result, it is difficult to automatically implement the translation. This paper makes a preliminary attempt in this aspect, based on the data set collected by ourselves, adopts deep encoder-decoder model, such as GRU, LSTM and Transformer models, to train our model. We compare the results of the three models, which have their own advantages and disadvantages. However, due to the size of the data set and the model itself, the effect is not very ideal, and still needs to be improved.
AB - Ancient poetry is an important part of Chinese culture. There have been projects like Jiuge to combine ancient poetry with deep learning. The language of ancient poetry is often refined, and it needs rich imagination to understand its meaning. As a result, it is difficult to automatically implement the translation. This paper makes a preliminary attempt in this aspect, based on the data set collected by ourselves, adopts deep encoder-decoder model, such as GRU, LSTM and Transformer models, to train our model. We compare the results of the three models, which have their own advantages and disadvantages. However, due to the size of the data set and the model itself, the effect is not very ideal, and still needs to be improved.
KW - Transformer
KW - ancient poetry
KW - deep learning
KW - machine translation
KW - natural language processing
KW - neural network
KW - seq2seq model
UR - https://www.scopus.com/pages/publications/85115444224
U2 - 10.1109/ICIEA51954.2021.9516256
DO - 10.1109/ICIEA51954.2021.9516256
M3 - 会议稿件
AN - SCOPUS:85115444224
T3 - Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
SP - 2032
EP - 2036
BT - Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
Y2 - 1 August 2021 through 4 August 2021
ER -