Mojca Brglez (Avtor), Špela Vintar (Avtor)

Povzetek

Neural machine translation systems have revolutionized translation processes in terms of quantity and speed in recent years, and they have even been claimed to achieve human parity. However, the quality of their output has also raised serious doubts and concerns, such as loss in lexical variation, evidence of “machine translationese”, and its effect on post-editing, which results in “post-editese”. In this study, we analyze the outputs of three English to Slovenian machine translation systems in terms of lexical diversity in three different genres. Using both quantitative and qualitative methods, we analyze one statistical and two neural systems, and we compare them to a human reference translation. Our quantitative analyses based on lexical diversity metrics show diverging results; however, translation systems, particularly neural ones, mostly exhibit larger lexical diversity than their human counterparts. Nevertheless, a qualitative method shows that these quantitative results are not always a reliable tool to assess true lexical diversity and that a lot of lexical “creativity”, especially by neural translation systems, is often unreliable, inconsistent, and misguided.

Ključne besede

machine translation;neural translation systems;lexical diversity;type-token ratio;measure of textual lexical diversity;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FF - Filozofska fakulteta
UDK: 81'25'322.4
COBISS: 100548099 Povezava se bo odprla v novem oknu
ISSN: 2078-2489
Št. ogledov: 93
Št. prenosov: 26
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: strojno prevajanje;nevronski prevajalniki;leksikalna diverziteta;razmerje različnic in pojavnic;merjenje besedilne leksikalne diverzitete;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-14
Letnik: ǂVol. ǂ13
Zvezek: ǂiss. ǂ2
Čas izdaje: 2022
DOI: 10.3390/info13020093
ID: 15584972