Pragmatic information in translation: a corpus-based study of tense and mood in English and German

TL;DR

This study examines the complex correspondence of tense and mood in English-German translation, highlighting challenges faced by human translators and implications for NLP models in capturing these linguistic features.

Contribution

It provides a corpus-based analysis revealing the intricacies and difficulties in translating tense and mood between English and German, informing NLP translation approaches.

Findings

No simple mapping exists for tense and mood between English and German.

Human translators face significant challenges in translating tense and mood.

Implications for improving rule-based and neural machine translation models.

Abstract

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.