How to evaluate machine translation: A review of automated and human metrics
E Chatzikoumi - Natural Language Engineering, 2020 - cambridge.org
This article presents the most up-to-date, influential automated, semiautomated and human
metrics used to evaluate the quality of machine translation (MT) output and provides the …
metrics used to evaluate the quality of machine translation (MT) output and provides the …
A comprehensive survey on various fully automatic machine translation evaluation metrics
The fast advancement in machine translation models necessitates the development of
accurate evaluation metrics that would allow researchers to track the progress in text …
accurate evaluation metrics that would allow researchers to track the progress in text …
Experts, errors, and context: A large-scale study of human evaluation for machine translation
Human evaluation of modern high-quality machine translation systems is a difficult problem,
and there is increasing evidence that inadequate evaluation procedures can lead to …
and there is increasing evidence that inadequate evaluation procedures can lead to …
A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM
Sentiment analysis is an essential task in natural language processing that involves
identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments …
identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments …
IndicMT eval: A dataset to meta-evaluate machine translation metrics for Indian languages
The rapid growth of machine translation (MT) systems necessitates meta-evaluations of
evaluation metrics to enable selection of those that best reflect MT quality. Unfortunately …
evaluation metrics to enable selection of those that best reflect MT quality. Unfortunately …
adaptmllm: Fine-tuning multilingual language models on low-resource languages with integrated llm playgrounds
The advent of Multilingual Language Models (MLLMs) and Large Language Models (LLMs)
has spawned innovation in many areas of natural language processing. Despite the exciting …
has spawned innovation in many areas of natural language processing. Despite the exciting …
Gpt-4 vs. human translators: A comprehensive evaluation of translation quality across languages, domains, and expertise levels
This study comprehensively evaluates the translation quality of Large Language Models
(LLMs), specifically GPT-4, against human translators of varying expertise levels across …
(LLMs), specifically GPT-4, against human translators of varying expertise levels across …
A product and process analysis of post-editor corrections on neural, statistical and rule-based machine translation output
M Koponen, L Salmi, M Nikulin - Machine Translation, 2019 - Springer
This paper presents a comparison of post-editing (PE) changes performed on English-to-
Finnish neural (NMT), rule-based (RBMT) and statistical machine translation (SMT) output …
Finnish neural (NMT), rule-based (RBMT) and statistical machine translation (SMT) output …
Translation quality and error recognition in professional neural machine translation post-editing
J Vardaro, M Schaeffer, S Hansen-Schirra - Informatics, 2019 - mdpi.com
This study aims to analyse how translation experts from the German department of the
European Commission's Directorate-General for Translation (DGT) identify and correct …
European Commission's Directorate-General for Translation (DGT) identify and correct …
Analysing terminology translation errors in statistical and neural machine translation
Terminology translation plays a critical role in domain-specific machine translation (MT).
Phrase-based statistical MT (PB-SMT) has been the dominant approach to MT for the past …
Phrase-based statistical MT (PB-SMT) has been the dominant approach to MT for the past …