We explore ways of identifying terms from monolingual texts and integrate them into investigating the contribution of terminology to translation quality.The researchers proposed a supervised learning method using common statistical measures for termhood and unithood as features to train classifiers for identifying terms in cross-domain and cross-language settings. On its basis, sequences of words from source texts (STs) and target texts (TTs) are aligned naively through a fuzzy matching mechanism for identifying the correctly translated term equivalents in student translations. Correlation analyses further show that normalized term occurrences in translations have weak linear relationship with translation quality in term of usefulness/transfer, terminology/style, idiomatic writing and target mechanics and near- and above-strong relationship with the overall translation quality. This method has demonstrated some reliability in automatically identifying terms in human translations. However, drawbacks in handling low frequency terms and term variations shall be dealt in the future.
@InProceedings{YUAN18.245, author = {YU Yuan and Yuze Gao and Yue Zhang and Serge Sharoff}, title = "{Cross-lingual Terminology Extraction for Translation Quality Estimation}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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