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 …
Translation quality assessment: A brief survey on manual and automatic methods
To facilitate effective translation modeling and translation studies, one of the crucial
questions to address is how to assess translation quality. From the perspectives of accuracy …
questions to address is how to assess translation quality. From the perspectives of accuracy …
Findings of the 2017 conference on machine translation (wmt17)
This paper presents the results of the WMT17 shared tasks, which included three machine
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …
[PDF][PDF] Findings of the 2014 workshop on statistical machine translation
This paper presents the results of the WMT14 shared tasks, which included a standard news
translation task, a separate medical translation task, a task for run-time estimation of …
translation task, a separate medical translation task, a task for run-time estimation of …
Unsupervised quality estimation for neural machine translation
Quality Estimation (QE) is an important component in making Machine Translation (MT)
useful in real-world applications, as it is aimed to inform the user on the quality of the MT …
useful in real-world applications, as it is aimed to inform the user on the quality of the MT …
TransQuest: Translation quality estimation with cross-lingual transformers
Recent years have seen big advances in the field of sentence-level quality estimation (QE),
largely as a result of using neural-based architectures. However, the majority of these …
largely as a result of using neural-based architectures. However, the majority of these …
[PDF][PDF] Predictor-estimator using multilevel task learning with stack propagation for neural quality estimation
In this paper, we present a two-stage neural quality estimation model that uses multilevel
task learning for translation quality estimation (QE) at the sentence, word, and phrase levels …
task learning for translation quality estimation (QE) at the sentence, word, and phrase levels …
Word error rate estimation for speech recognition: e-WER
Measuring the performance of automatic speech recognition (ASR) systems requires
manually transcribed data in order to compute the word error rate (WER), which is often time …
manually transcribed data in order to compute the word error rate (WER), which is often time …
[PDF][PDF] Multi-level translation quality prediction with quest++
This paper presents QUEST++, an open source tool for quality estimation which can predict
quality for texts at word, sentence and document level. It also provides pipelined processing …
quality for texts at word, sentence and document level. It also provides pipelined processing …
UMIC: An unreferenced metric for image captioning via contrastive learning
Despite the success of various text generation metrics such as BERTScore, it is still difficult
to evaluate the image captions without enough reference captions due to the diversity of the …
to evaluate the image captions without enough reference captions due to the diversity of the …