Abstract
With the rapid progress of deep learning in multilingual language processing, there has been a growing interest in reference-free machine translation evaluation, where source texts are directly compared with system translations. In this paper, we design a reference-free metric that is based only on a target-side language model for segment-level and system-level machine translation evaluations respectively, and it is found out that promising results could be achieved when only the target-side language model is used in such evaluations. From the experimental results on all the 18 language pairs of the WMT19 news translation shared task, it is interesting to see that the designed metrics with the multilingual model XLM-R get very promising results (best segment-level mean score on the from-English language pairs, and best system-level mean scores on the from-English and none-English language pairs) when the current SOTA metrics that we know are chosen for comparison.
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Zhang, M. et al. (2022). Target-Side Language Model for Reference-Free Machine Translation Evaluation. In: Xiao, T., Pino, J. (eds) Machine Translation. CCMT 2022. Communications in Computer and Information Science, vol 1671. Springer, Singapore. https://doi.org/10.1007/978-981-19-7960-6_5
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DOI: https://doi.org/10.1007/978-981-19-7960-6_5
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