Lexical diversity in statistical and neural machine translation

Mojca Brglez (Author), Špela Vintar (Author)

Abstract

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.

Keywords

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

Data

Language:	English
Year of publishing:	2022
Typology:	1.01 - Original Scientific Article
Organization:	UL FF - Faculty of Arts
UDC:	81'25'322.4
COBISS:	100548099
ISSN:	2078-2489
Views:	93
Downloads:	26
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	strojno prevajanje;nevronski prevajalniki;leksikalna diverziteta;razmerje različnic in pojavnic;merjenje besedilne leksikalne diverzitete;
Type (COBISS):	Article
Pages:	str. 1-14
Volume:	ǂVol. ǂ13
Issue:	ǂiss. ǂ2
Chronology:	2022
DOI:	10.3390/info13020093
ID:	15584972