Machine translation (MT) is an important task in natural language processing (NLP), as it
automates the translation process and reduces the reliance on human translators. With the …

Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to
compare semantic understanding of different NLP systems. In this survey paper, we provide …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

This paper presents the results of the WMT21 Metrics Shared Task. Participants were asked
to score the outputs of the translation systems competing in the WMT21 News Translation …

This paper presents the results of the news translation task, the multilingual low-resource
translation for Indo-European languages, the triangular translation task, and the automatic …

Unlike literal expressions, idioms' meanings do not directly follow from their parts, posing a
challenge for neural machine translation (NMT). NMT models are often unable to translate …

This paper offers a fine-grained analysis of the machine translation outputs in the context of
the Shared Task at the 8th Conference of Machine Translation (WMT23). Building on the …

Machine learning (ML) models can fail in unexpected ways in the real world, but not all
model failures are equal. With finite time and resources, ML practitioners are forced to …

We employ a linguistically motivated challenge set in order to evaluate the state-of-the-art
machine translation metrics submitted to the Metrics Shared Task of the 7th Conference for …

Text generation often involves producing coherent and grammatically correct texts that also
satisfy a given set of semantic constraints. While most approaches for conditional text …