MT Evaluation in the Context of Language Complexity

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dc.contributor.authorMunková, Dášacs
dc.contributor.authorMunk, Michalcs
dc.contributor.authorBenko, Lubomírcs
dc.contributor.authorŠťastný, Jiřícs
dc.coverage.issue1cs
dc.coverage.volume2021cs
dc.date.accessioned2022-03-16T07:53:59Z
dc.date.available2022-03-16T07:53:59Z
dc.date.issued2022-01-01cs
dc.description.abstractThe paper focuses on investigating the impact of artificial agent (machine translator) on human agent (posteditor) using a proposed methodology, which is based on language complexity measures, POS tags, frequent tagsets, association rules, and their summarization. We examine this impact from the point of view of language complexity in terms of word and sentence structure. By the proposed methodology, we analyzed 24 733 tags of English to Slovak translations of technical texts, corresponding to the output of two MT systems (Google Translate and the European Commission’s MT tool). We used both manual (adequacy and fluency) and semiautomatic (HTER metric) MT evaluation measures as the criteria for validity. We show that the proposed methodology is valid based on the evaluation of frequent tagsets and rules of MT outputs produced by Google Translate or of the European Commission’s MT tool, and both postedited MT (PEMT) outputs using baseline methods. Our results have also shown that PEMT output produced by Google Translate is characterized by more frequent tagsets such as verbs in the infinitive with modal verbs compared to its MT output, which is characterized by masculine, inanimate nouns in locative of singular. In the MT output, produced by the European Commission’s MT tool, the most frequent tagset was verbs in the infinitive compared to its postedited MT output, where verbs in imperative and the second person of plural occurred. These findings are also obtained from the use of the proposed methodology for MT evaluation. The contribution of the proposed methodology is an identification of systematic not random errors. Additionally, the study can also serve as information for optimizing the translation process using postediting.en
dc.formattextcs
dc.format.extent1-15cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationCOMPLEXITY. 2022, vol. 2021, issue 1, p. 1-15.en
dc.identifier.doi10.1155/2021/2806108cs
dc.identifier.issn1076-2787cs
dc.identifier.other176667cs
dc.identifier.urihttp://hdl.handle.net/11012/203982
dc.language.isoencs
dc.publisherHindawics
dc.relation.ispartofCOMPLEXITYcs
dc.relation.urihttps://www.hindawi.com/journals/complexity/2021/2806108/cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1076-2787/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectartificial agenten
dc.subjectassociation rulesen
dc.subjectlanguage complexity measuresen
dc.subjectmachine translatoren
dc.titleMT Evaluation in the Context of Language Complexityen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-176667en
sync.item.dbtypeVAVen
sync.item.insts2022.04.25 20:53:33en
sync.item.modts2022.04.25 20:14:16en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automatizace a informatikycs
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