医療文献における機械翻訳の質: 標準化された評価指標に基づく分析
Pascal Block1, Johanna Schaefer1, Felix Maurer1
1Goethe University Frankfurt, University Medicine, Institute of Medical Informatics (IMI), Frankfurt am Main, Germany.
Studies in health technology and informatics
|September 3, 2025
まとめ
普通の機械翻訳システムは 珍しい病気の医療文献に適した品質を提供しており,英語の翻訳は最高の性能です. METEORとCOMETの指標は中等から高い品質を示していますが,BLEUのスコアは低いです.
科学分野:
- 医療情報工学
- 自然言語処理
背景:
- SE-ATLASの利用は言語の多様性を反映しています.
- SE-ATLASのウェブサイトはドイツに関する情報を提供していますが,ドイツ語のみのコンテンツは,ドイツ語以外の言語の障壁を生み出しています.
研究 の 目的:
- 医療文献の一般的な機械翻訳システムの質を評価する.
- 言語の障壁を克服して 珍しい病気の情報を入手できるかどうか
主な方法:
- ドイツ語から英語,フランス語,ポーランド語への医療テキスト翻訳のためのDeepL,ChatGPT,およびGoogle Translateの分析.
- 標準化されたメトリックを用いた客観的な品質評価: BLEU,METEOR,COMET,参照翻訳と比較
主要な成果:
- METEORとCOMETの指標は中等から高い翻訳品質を示したが,BLEUのスコアは低い.
- 英語の翻訳はフランス語とポーランド語に比べて一般的に高い評価を得ました.
結論:
- この3つのシステムでは,翻訳の質に統計的に有意な差はなく,すべて受け入れられる結果が得られました.
- データ保護,コスト,統合といった 翻訳の品質以外の要因が システムの選択を導くべきである.
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