2020/07/10 by Anita Ramm, Ramm, Anita, Ekaterina Lapshinova‐Koltunski +4
Arts and Humanities · Computer Science · #Natural Language Processing Techniques #Topic Modeling #Translation Studies and Practices #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.2007.05234
Technical Report of CIS, LMU Munich. September 19th, 2019
arxiv created 2020/07/10 · arxiv updated 2020/07/13
Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research. We consider the correspondence between English and German tense and mood in translation. Human translators do not find this correspondence easy, and as we will show through careful analysis, there are no simplistic ways to map tense and mood from one language to another. Our observations about the challenges of human translation of tense and mood have important implications for multilingual NLP. Of particular importance is the challenge of modeling tense and mood in rule-based, phrase-based statistical and neural machine translation.