earticle

영어

This study compares and analyzes machine and human translation, then post-edited machine translation and revised human translation. Source texts were composed of colloquial and written styles, respectively, since the results of MT and MTPE may vary. Evaluation and qualitative assessment were manually undertaken based on the TQA criteria proposed by Han (2021). Professional translators codded and scored errors, and Korean-Russsian machine translation and human translation differ in frequency and distribution of errors. The form-related 'accuracy' errors were distinctive in machine translation. In contrast, errors in meaning-related 'clarity/comprehensibility' occurred more with human translation. After machine translation is post-edited and human translation is revised, the quality of each output was significantly improved; however, some error types somewhat increased despite post-editing and revision efforts. Reviewers tended to assess human translators' clarification and explication attempts as excessive intervention. Moreover, post-edited machine translation and revised human translation showed little difference in evaluation scores, indicating that specialized training for post-editors (or reviewers) will inevitably enhance machine translation quality.