This work presents our efforts to reproduce the results of the human evaluation experiment presented in the paper of Vamvas and Sennrich (2022), which evaluated an automatic system detecting over- and undertranslations (translations containing more or less information than the original) in machine translation (MT) outputs. Despite the high quality of the documentation and code provided by the authors, we discuss some problems we found in reproducing the exact experimental setup and offer recommendations for improving reproducibility. Our replicated results generally confirm the conclusions of the original study, but in some cases statistically significant differences were observed, suggesting a high variability of human annotation
Quality Estimation (QE) and error analysis of Machine Translation (MT) output remain active areas in...
This work describes analysis of nature and causes of MT errors observed by different evaluators unde...
Abstract — Machine translation is an inevitable field of Natural Language Processing, which includes...
The evaluation of errors made by Machine Translation (MT) systems still needs hu-man effort despite ...
Machine translation (MT) has been an important field of research in the last decades and is currentl...
Error analysis is a means to assess machine translation output in qualitative terms, which can be us...
Document-level human evaluation of machine translation (MT) has been raising interest in the communi...
Existing automated MT evaluation methods often require expert human translations. These are produced...
Automatic evaluation of machine translation (MT) is based on the idea that the quality of the MT out...
International audienceIn this paper, we present a freely available corpus of automatic translations ...
In order to improve the symbiosis between machine translation (MT) system and post-editor, it is not...
This paper examines two techniques of manual evaluation that can be used to identify error types of ...
The increasing role of Post-editing (PE) as a way of improving Machine Translation (MT) output and a...
This paper describes some of the kinds of pre-dictable errors in Machine Translation (MT). It then d...
Since the emergence of the first fully automatic machine translation (MT) systems over fifty years a...
Quality Estimation (QE) and error analysis of Machine Translation (MT) output remain active areas in...
This work describes analysis of nature and causes of MT errors observed by different evaluators unde...
Abstract — Machine translation is an inevitable field of Natural Language Processing, which includes...
The evaluation of errors made by Machine Translation (MT) systems still needs hu-man effort despite ...
Machine translation (MT) has been an important field of research in the last decades and is currentl...
Error analysis is a means to assess machine translation output in qualitative terms, which can be us...
Document-level human evaluation of machine translation (MT) has been raising interest in the communi...
Existing automated MT evaluation methods often require expert human translations. These are produced...
Automatic evaluation of machine translation (MT) is based on the idea that the quality of the MT out...
International audienceIn this paper, we present a freely available corpus of automatic translations ...
In order to improve the symbiosis between machine translation (MT) system and post-editor, it is not...
This paper examines two techniques of manual evaluation that can be used to identify error types of ...
The increasing role of Post-editing (PE) as a way of improving Machine Translation (MT) output and a...
This paper describes some of the kinds of pre-dictable errors in Machine Translation (MT). It then d...
Since the emergence of the first fully automatic machine translation (MT) systems over fifty years a...
Quality Estimation (QE) and error analysis of Machine Translation (MT) output remain active areas in...
This work describes analysis of nature and causes of MT errors observed by different evaluators unde...
Abstract — Machine translation is an inevitable field of Natural Language Processing, which includes...