AI RESEARCH
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation
arXiv CS.CL
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ArXi:2601.07338v2 Announce Type: replace Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Network Services, literature etc. In these scenarios, translations often require handling non-literal expressions, leading to the inaccuracy of MT metrics. To systematically investigate the reliability of MT metrics, we first curate a meta-evaluation dataset focused on non-literal translations, namely