KpopMT: Translation Dataset with Terminology for Kpop Fandom
This addresses translation challenges for social groups with unique terminologies, though it is incremental as it focuses on a specific domain.
The paper tackles the problem of translating group-specific terminology in social groups by creating the KpopMT dataset with 1,000 expert-translated Korean-to-English examples from Kpop fandom, and evaluation shows existing translation systems perform poorly on it.
While machines learn from existing corpora, humans have the unique capability to establish and accept new language systems. This makes human form unique language systems within social groups. Aligning with this, we focus on a gap remaining in addressing translation challenges within social groups, where in-group members utilize unique terminologies. We propose KpopMT dataset, which aims to fill this gap by enabling precise terminology translation, choosing Kpop fandom as an initiative for social groups given its global popularity. Expert translators provide 1k English translations for Korean posts and comments, each annotated with specific terminology within social groups' language systems. We evaluate existing translation systems including GPT models on KpopMT to identify their failure cases. Results show overall low scores, underscoring the challenges of reflecting group-specific terminologies and styles in translation. We make KpopMT publicly available.