Metric for automatic machine translation evaluation based on universal sentence representations

概要

Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Al-though it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.

論文種別
発表文献
Proceedings of the NAACL 2018 Student Research Workshop (NAACL 2018 SRW)
梶原智之
梶原智之
招へい助教

自然言語処理。特に、テキスト平易化、言い換え、意味的文間類似度、品質推定。