Current Shortcomings of Machine Translation in S panish and B ulgarian Vis-à-vis E nglish (2022)

Abstract: In late 2016, Google Translate (GT), widely considered a machine translation leader, replaced its statistical machine translation (SMT) functions with a neural machine translation (NMT) model for many large languages, including Spanish, with other languages following thereafter. Whereas the capabilities of GT had previously advanced incrementally, this switch to NMT resulted in seemingly exponential improvement. However, half a dozen years later, while recognizing GT’s usefulness, it is also imperative to systematically evaluate ongoing shortcomings, including determining which challenges may reasonably be presumed as superable over time and those which, following a multiyear tracking study, prove unlikely ever to be fully resolved. While the research in question principally explores Spanish-English-Spanish machine translation, this paper examines similar problems with Bulgarian-English-Bulgarian GT renditions. Better understanding both the strengths and weaknesses of current machine translation applications is fundamental to knowing when such non-human natural language processing (NLP) technology is capable of performing all or most of a given task, and when heavy, perhaps even exclusive human intervention is still required.

## theoretical introduction and historical overview the genesis of this study lies in events that, while years in the making, came to light fully in late 2016, when programmers behind the scenes switched the online machine translation service google translate (gt) from one employing statistical machine translation (smt) to one relying on the company's newly completed neural machine translation (nmt) system @xcite . rather than featuring different modules, nmt utilizes a single, streamlined system that contains only an encoder, which analyzes the training data (mostly bilingual corpora), and a decoder, which applies this analysis to a new source-language text and renders it into the chosen target language. while the encoder assigns individual words and other features numerical qualities, the decoder considers texts to be translated at the full sentence level, rather than according to separate words or phrases as with the smt models @xcite . this seeming simplicity should not obscure the fact that nmt is not only extremely complex, but, given that the representation of the data is strictly numerical, it is not completely understood even by those who have written the algorithms leading to the vectors of numbers involved in the work of encoding the bilingual texts @xcite . while nmt can compete with human translators on tasks involving highly repetitive structures (e.g. legal documents, economic texts), less common and more creative, more novel content can lead to serious meaning errors. in other words, adequacy may suffer even if the fluency of the resulting translation may be acceptable. the main cause of this difficulty on the part of computers to engage successfully in natural language processing (nlp) is ambiguity (lexico-semantic, morphosyntactic, etc.) @xcite . a number of examples displaying this phenomenon are considered in this paper. ## overview of research the purpose of the larger research projectbased mainly on english and spanishis to determine not only what nmt can and cannot do, both generally and specifically, but also what improvements might occur over the next five years or so. as this research focuses largely on the written word, a thorough assessment of these matters requires a systematic evaluation of the different categories involved, namely expository writing, descriptive/narrative writing, and persuasive writing via texts from numerous subcategories in each case. this allows for methodical contrasting and comparing of the results yielded by gt, some of which are unique to the relationship between english and spanish, whereas others have features that can be extrapolated to other languages, including bulgarian. ## presentation, discussion, and analysis of research data initial work drawing on material from the above-mentioned categories will now allow for a discussion of gt results stemming from a variety of texts. while gt is capable of performing felicitous translations in many content areas, and while it has arguably improved vastly in all areas since the switch to nmt beginning in 2016, many renditions continue to be problematic to one degree or another. while many such instances are given throughout this paper, it is appropriate at this point to share two such examples (i.e. one that is considered to be a suitable, even excellent translation by gt, and another that is decidedly flawed). both happen to be from english to spanish. the first is from an economic report published by the business and finance website cnbc.com @xcite : ## conclusions this paper is part of a larger, long-term study whose central focus is the ability (or inability) of google translate (gt) to render acceptable translations among multiple written genres between english and spanish and vice versa. some of the challenges relating to this pair of languages extend to others. while many gt capabilities have been greatly enhanced since the service's 2016 shift from the use of statistical machine translation (smt) to a system of neural machine translation (nmt), this does not mean that all such renditions are perfect or even acceptable, or that its performance based on the perceived complexity or simplicity of source texts is predictable. some economic texts, for instance, are rather intricate, but gt more often than not produces very usable english-spanish-english results. in contrast, some seemingly simple texts are badly distorted when run through gt, even when no ambiguity is readily apparent. this is surely a reflection of the fact that even apparently uncomplicated human language is more involved than its speakers typically realize. added to this is the fact that even relatively simple ideas can be expressed in such a variety of ways that no database could contain all the possibilities, let alone adequate past translations of them. this means that any solution to mistranslations could either be years, even decades away, or, surely in some cases, never be attainable at all, signifying that human translators will need to continue occupying an indispensable role in the translation process for the foreseeable future. while ascertaining some of these matters to the degree possible is the objective of the larger study, various patterns have already begun to suggest themselves and have been demonstrated to a modest degree in this paper via several examples featuring spanish, english, and bulgarian.