NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures
Abstract
Being able to rank the similarity of short text segments is an interesting bonus feature of neural machine translation. Translation-based similarity measures include direct and pivot translation probability, as well as translation cross-likelihood, which has not been studied so far. We analyze these measures in the common framework of multilingual NMT, releasing the NMTScore library. Compared to baselines such as sentence embeddings, translation-based measures prove competitive in paraphrase identification and are more robust against adversarial or multilingual input, especially if proper normalization is applied. When used for reference-based evaluation of data-to-text generation in 2 tasks and 17 languages, translation-based measures show a relatively high correlation to human judgments.
BibTeX
@inproceedings{vamvas-sennrich-2022-nmtscore,
title = "{NMTS}core: A Multilingual Analysis of Translation-based Text Similarity Measures",
author = "Vamvas, Jannis and
Sennrich, Rico",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.15/",
doi = "10.18653/v1/2022.findings-emnlp.15",
pages = "198--213"
}