Analysis on the Recent Trends in Machine Translation
DOI:
https://doi.org/10.54097/hset.v16i.2228Abstract
Machine translation is to translate one language into another language, which has undergone a great evolution. The model of machine translation has been continuously improved, aiming to make the translation effect closer to the artificial translation. This article briefly summarizes the development history of machine translation, and introduces the main models of each stage of development. The initial machine translation mode is the Rule Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). Recent mainstream translation approach enables Neural Machine Translation (NMT). It includes the input and the output, attention mechanism, and BLEU evaluation method. On this basis, there are also many expansion and innovation models, such as GPKD and other models to improve the evaluation effect. In general, machine translation can replace a part of human translation. However, it cannot completely replace human beings, because of the different human thinking and machine logic. People and machines have to cooperate with each other to improve the common efficiency.
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Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. A neural probabilistic language model. Journal of machine learning research, 3 (Feb): 1137-1155, 2003. Google Scholar Google Scholar Digital Library Digital Library.
Kalchbrenner, N., & Blunsom, P. (2013, October). Recurrent continuous translation models. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1700-1709).
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002, July). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311-318).
Denkowski, M., & Lavie, A. (2014, June). Meteor universal: Language specific translation evaluation for any target language. In Proceedings of the ninth workshop on statistical machine translation (pp. 376-380).
He, H., Wang, Q., Yu, Z., Zhao, Y., Zhang, J., & Zong, C. (2021, May). Synchronous interactive decoding for multilingual neural machine translation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 14, pp. 12981-12988).
Zhang, F., Tu, M., & Yan, J. (2021, May). Accelerating Neural Machine Translation with Partial Word Embedding Compression. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 16, pp. 14356-14364).
Li, Y., Lin, Y., Xiao, T., & Zhu, J. (2021, May). An efficient transformer decoder with compressed sub-layers. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 15, pp. 13315-13323).
Li, B., Wang, Z., Liu, H., Du, Q., Xiao, T., Zhang, C., & Zhu, J. (2021, May). Learning light-weight translation models from deep transformer. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 15, pp. 13217-13225).
Chen, G., Ma, S., Chen, Y., Zhang, D., Pan, J., Wang, W., & Wei, F. (2022, May). Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 142-157).
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