arXiv:2606.05421v1 Announce Type: new 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
The proliferation of advanced machine translation models necessitates new benchmarks that go beyond basic accuracy, focusing on nuanced aspects like text complexity.
This research provides a framework for evaluating machine translation's ability to handle linguistic nuance, which is crucial for high-stakes applications and global communication.
The focus for evaluating machine translation quality expands beyond simple fidelity to include the preservation or adaptation of text complexity, introducing new performance metrics.
- · Machine Translation Developers
- · Multilingual Content Creators
- · Linguists
- · Machine Translation Models with Low Complexity Retention
- · Users relying on unsophisticated translation
Improved machine translation systems will become more adept at translating complex, nuanced texts without information loss.
This enhanced capability will facilitate more sophisticated cross-cultural communication and knowledge transfer, increasing demand for such AI services.
The ability to accurately translate complex thought across languages could accelerate scientific and cultural integration, potentially diminishing language as a barrier to global collaboration.
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