This paper presents the results of the WMT11 shared tasks, which included a translation
task, a system combination task, and a task for machine translation evaluation metrics. We …

Current measures for evaluating text simplification systems focus on evaluating lexical text
aspects, neglecting its structural aspects. In this paper we propose the first measure to …

Background Recent advances in representation learning have enabled large strides in
natural language understanding; However, verbal reasoning remains a challenge for state …

The evaluation of image caption quality is a challenging task, which requires the
assessment of two main aspects in a caption: adequacy and fluency. These quality aspects …

This study explores the possibility of replacing the costly and time-consuming human
evaluation of the grammaticality and meaning preservation of the output of text simplification …

This paper presents the results of the shared task of the Workshop on Quality Assessment
for Text Simplification (QATS), which consisted in automatically assigning one of the three …

We introduce XMEANT—a new cross-lingual version of the semantic frame based MT
evaluation metric MEANT—which can correlate even more closely with human adequacy …

ThelinguisticallytransparentMEANTand UMEANT metrics are tunable, simple yet highly
effective, fully automatic approximation to the human HMEANT MT evaluation metric which …

VerbNet—the most extensive online verb lexicon currently available for English—has
proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual …

We present the first ever results showing that tuning a machine translation system against a
semantic frame based objective function, MEANT, produces more robustly adequate …