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Bentivogli, L., Arianna, B., Mauro, C., & Marcello, F. (2016). Neural Versus Phrase-Based Machine Translation Quality: A Case Study. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, 1-5 November 2016, 257-267.
https://doi.org/10.18653/v1/D16-1025

has been cited by the following article:

• TITLE: Comparing and Analyzing Cohesive Devices of SMT and NMT from Chinese to English: A Diachronic Approach

  AUTHORS: Jiao Liu

  KEYWORDS: Statistical Machine Translation, Neural Machine Translation, Cohesive Devices

  JOURNAL NAME: Open Journal of Modern Linguistics, Vol.10 No.6, November 26, 2020

  ABSTRACT: This work presents a detailed comparison and analysis of the usage of cohesive devices by three Machine Translation systems from Chinese to English, in both SMT and NMT situations. By both a general analysis of sentence length as well as cohesive devices and detailed analysis of a sentence translation in SMT and NMT with human translation as a reference, it is shown that, compared with SMT, NMT system is better at handling cohesive ties such as additive, adverbs and pronouns; however, both SMT and NMT underperform at dealing with demonstratives and lexical cohesion. This suggests an evidence of improved translation quality and the necessity of pre-editing and post-editing cohesive devices in MT translations.