3 papers
cs.CL2026
Translating the Untranslatable: An Operationalizable Ontology for Untranslatability
Jacob Bremerman, Brihi Joshi, Hirona Arai +2
Untranslatability, cases where meaning cannot be directly preserved across languages, is well-studied in linguistics but underexplored in NLP. As machine translation (MT) systems i…
cs.CL2022
Machine Translation Robustness to Natural Asemantic Variation
Jacob Bremerman, Xiang Ren, Jonathan May
Current Machine Translation (MT) models still struggle with more challenging input, such as noisy data and tail-end words and phrases. Several works have addressed this robustness…
cs.CL2021
The Multilingual TEDx Corpus for Speech Recognition and Translation
Elizabeth Salesky, Matthew Wiesner, Jacob Bremerman +5
We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages. The corpus is a co…