PETra: A Multilingual Corpus of Pragmatic Explicitation in Translation

Abstract

Translators often enrich texts with background details that make implicit cultural meanings explicit for new audiences. This phenomenon, known as pragmatic explicitation, has been widely discussed in translation theory but rarely modeled computationally. We introduce PeTra, the first multilingual corpus and detection framework for pragmatic explicitation. The corpus consists of 2,900 sentence pairs from TED-Multi and Europarl, covers twelve language pairs, and includes additions such as entity descriptions, measurement conversions, and translator remarks. We identify candidates through null alignments and refine them using active learning with human annotation. Our results show that entity and system-level (e.g., metric conversions) explicitations are most frequent, and that active learning improves classifier accuracy by 7-8 percentage points, achieving up to 0.88 accuracy and 0.82 F1 for the best transfer languages. PeTra establishes pragmatic explicitation as a measurable, cross-linguistic phenomenon and takes a step towards building culturally aware machine translation.

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