Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTTranslationSimilar Papers 제목 키워드 기반
Evaluating Machine Translation in a Usage Scenario
In this document we report on a user-scenario-based evaluation aiming at assessing the performance of machine translation (MT) systems in a real context of use. We describe a sequel of experiments that has been performed…
Machine TranslationTranslationQTLeap WSD/NED Corpora: Semantic Annotation of Parallel Corpora in Six Languages
This work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambi…
Cross-Lingual TransferEntity DisambiguationGeneral ClassificationLEMMA+7Bulgarian-English and English-Bulgarian Machine Translation: System Design and Evaluation
The paper presents a deep factored machine translation (MT) system between English and Bulgarian languages in both directions. The MT system is hybrid. It consists of three main steps: (1) the source-language text is lin…
Machine TranslationTranslationApplying the Cognitive Machine Translation Evaluation Approach to Arabic
The goal of the cognitive machine translation (MT) evaluation approach is to build classifiers which assign post-editing effort scores to new texts. The approach helps estimate fair compensation for post-editors in the t…
Machine TranslationTranslationExpanding Parallel Resources for Medium-Density Languages for Free
We discuss a previously proposed method for augmenting parallel corpora of limited size for the purposes of machine translation through monolingual paraphrasing of the source language. We develop a three-stage shallow pa…
Machine TranslationMorphological AnalysisTranslation