An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

Lexical ambiguity is one of the many challenging linguistic phenomena involved in translation, i.e., translating an ambiguous word with its correct sense. In this respect, previous work has shown that the translation quality of neural machine translation systems can be improved by explicitly modeling the senses of ambiguous words. Recently, several evaluation test sets have been proposed to measure the word sense disambiguation (WSD) capability of machine translation systems. However, to date, these evaluation test sets do not include any training data that would provide a fair setup measuring the sense distributions present within the training data itself. In this paper, we present an evaluation benchmark on WSD for machine translation for 10 language pairs, comprising training data with known sense distributions. Our approach for the construction of the benchmark builds upon the wide-coverage multilingual sense inventory of BabelNet, the multilingual neural parsing pipeline TurkuNLP, and the OPUS collection of translated texts from the web. The test suite is available at http://github.com/Helsinki-NLP/MuCoW.
Alkuperäiskielienglanti
OtsikkoProceedings of The 12th Language Resources and Evaluation Conference
Sivumäärä8
JulkaisupaikkaMarseille, France
KustantajaEuropean Language Resources Association (ELRA)
Julkaisupäivä1 toukokuuta 2020
Sivut3668-3675
TilaJulkaistu - 1 toukokuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa

Projektit

Siteeraa tätä

Raganato, A., Scherrer, Y., & Tiedemann, J. (2020). An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems. teoksessa Proceedings of The 12th Language Resources and Evaluation Conference (Sivut 3668-3675). Marseille, France: European Language Resources Association (ELRA).