Machine translation fails to return identical source text upon round-trip translation due to semantic divergence and ambiguity resolution
the verdict
INSUFFICIENT LEANING
refutedsupported
the weight of evidence
2 sources for · 0 against
The retrieved literature partially supports the claim by noting that round-trip translated texts differ significantly from original texts and that machine translation systems encounter semantic errors involving word sense mistranslations, but the sources do not fully establish the causal mechanisms of ambiguity resolution as claimed.
Semantic errors in machine translation (MT) occur when the output is fluent but fails to preserve meaning—by mistranslating word senses, omitting or adding content, or generating “hallucinated” information not supported by the source. Such errors are especially problematic because they can look grammatically perfect while being semantically wrong, making them hard to detect during post-editing. Research on neural machine translation (NMT) highlights that adequacy problems such as omissions and additions can appear in otherwise fluent output, masking meaning loss. Studies on hallucinations show
Machine Translated Text Detection Through Text Similarity with Round-Trip Translation | CiNii Research 検索 タイトル 人物/団体名 著者ID/研究者番号 所属機関 ISSN DOI 期間 〜 本文リンク 本文リンクあり データソース JaLC IRDB Crossref DataCite NDLサーチ NDLデジコレ（旧NII-ELS） RUDA JDCat NINJAL CiNii Articles CiNii Books NACSIS-CAT/ILL DBpedia KAKEN e-Rad Integbio PubMed LSDB Archive 極地研ADS 極地研学術DB OpenAIRE 公共データカタログ すべて 研究データ 論文 本 博士論文 プロジェクト 人物 > 人物検索機能について 詳細検索 閉じる CiNii Researchナレッジグラフ検索機能（試行版）をCiNii Labsにて公開しました 「研究データ」「根拠データ」の収録について CiNii Books機能統合対応の追加実施をいたしました Machine Translated Text Detection Through Text Similarity with Round-Trip Translation DOI Hoang-Quoc Nguyen-Son Tran Thao Phuong Seira Hidano Ishita Gupta Shinsaku Kiyomoto 書誌事項 公開日 2021-01-01 DOI 10.18653/v1/2021.naacl-main.462 公開者 Association for Computational Linguistics (ACL) 説明 Translated texts have been used for malicious purposes, i.e., plagiarism or fake reviews.
Existing detectors have been built around a specific translator (e.g., Google) but fail to detect a translated text from a strange translator. If we use the same translator, the translated text is similar to its round-trip translation, which is when text is translated into another language and translated back into the original language. However, a round-trip translated text is significantly different from the original text or a translated text using a strange translator. Hence, we propose a detector using text similarity with round-trip translation (TSRT). TSRT achieves 86.9% accuracy in detecting a translated text from a strange translator.
Everything we examined (2)
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