ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translation

Joseph Marvin Imperial; Junhong Liang; Belal Shoer; Abdullah Barayan; Rodrigo Wilkens; Omar Mussa; Dawn Knight; Eugénio Ribeiro; Ekaterina Kochmar; Sowmya Vajjala; Fernando Alva-Manchego; Harish Tayyar Madabushi

arXiv · 2606.05421

ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translation

Joseph Marvin Imperial·Junhong Liang·Belal Shoer·Abdullah Barayan·Rodrigo Wilkens·Omar Mussa·Dawn Knight·Eugénio Ribeiro·Ekaterina Kochmar·Sowmya Vajjala·Fernando Alva-Manchego·Harish Tayyar Madabushi

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Abstract

When a text is translated, does the translation retain the complexity of the original? We introduce ComplexityMT, a new challenge for assessing how text complexity and machine translation interact with and influence each other, using the Common European Framework of Reference for Languages (CEFR) levels as the measure of text complexity. Across six languages, including Arabic, Dutch, English, French, Hindi, and Russian, we evaluate three open-weight models, one closed model, and a commercial machine translation system on two tasks: i) correlation of CEFR with translation difficulty, and ii) shifts in CEFR levels of the source texts. Our experiments show that higher CEFR levels make texts more difficult to translate and that machine translation lowers the target text's CEFR level for most languages at the document level. These findings provide new insights for researchers and practitioners working on multilingual pedagogical content generation and MT difficulty estimation.

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Joseph Marvin Imperial, Junhong Liang, Belal Shoer, Abdullah Barayan, Rodrigo Wilkens, Omar Mussa, Dawn Knight, Eugénio Ribeiro, Ekaterina Kochmar, Sowmya Vajjala, Fernando Alva-Manchego, Harish Tayyar Madabushi. 2026-06-03. ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translation. https://arxiv.org/abs/2606.05421

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