Year: 2022
Type: conference-paper
Abstract: We release NTREX-128, a data set for machine translation (MT) evaluation from English into a total of 128 target languages.The paper describes the data creation process and proposes a quality filtering method based on human evaluation.We show experimental results which confirm that the directionality of test sets translation indeed plays an important role wrt. the usefulness of the corresponding metrics' scores.Thus, we recommend that the NTREX-128 data set should be used for evaluation of Englishsourced translation models but not in reverse direction.The test set release introduces another benchmark for the evaluation of massively multilingual machine translation research.
Authors: Christian Federmann, Tom Kocmi, Ying Xin
Institution: Microsoft (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404784207