Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
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. Although 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationSentenceTranslationSimilar Papers 제목 키워드 기반
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training
Automatic metrics play a crucial role in machine translation. Despite the widespread use of n-gram-based metrics, there has been a recent surge in the development of pre-trained model-based metrics that focus on measurin…
Machine TranslationSentenceTranslationEvaluating the Efficacy of Length-Controllable Machine Translation
Length-controllable machine translation is a type of constrained translation. It aims to contain the original meaning as much as possible while controlling the length of the translation. We can use automatic summarizatio…
Machine TranslationTranslationRevisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages
While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated almost exclusively for English and other high-resource languages. This na…
Machine TranslationText SummarizationTangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics
Automatic metrics are fundamental for the development and evaluation of machine translation systems. Judging whether, and to what extent, automatic metrics concur with the gold standard of human evaluation is not a strai…
Machine TranslationTranslationAn Automatic Evaluation of the WMT22 General Machine Translation Task
This report presents an automatic evaluation of the general machine translation task of the Seventh Conference on Machine Translation (WMT22). It evaluates a total of 185 systems for 21 translation directions including h…
Machine TranslationTranslation