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+ ---
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+ # Dataset Card for DocBlocks
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+
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+ DocBlocks is a high-quality, multilingual document-level machine translation (MT) dataset designed to fine-tune large language models (LLMs) on long-context translation tasks. Unlike traditional sentence-level datasets, it contains full documents with natural discourse structures and contextual alignment, helping models maintain coherence, consistency, and high translation quality across longer texts.
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+ - **Curated by:** Instituto Superior Técnico, Instituto de Telecomunicações, Carnegie Mellon University and Unbabel;
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+ - **Language(s) (NLP):** English, German, Spanish, French, Italian, Dutch, Portuguese, Russian, Korean, Chinese;
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+ - **License:** DocBlocks includes data from the following sources: **IWSLT**, **Europarl**, **News Commentary**, **GuoFeng**, and **BWB**. For licensing information, please refer to the official documentation or websites of each source.
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+
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+ ### Dataset Details
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+
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+ * `conversations` - The user and assistant dialogue turns, following an instruction-based format suitable for LLM fine-tuning;
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+ * `sources` - The original source text in the source language;
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+ * `references` - The human-translated target/reference text in the target language;
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+ * `language_pair` - The language direction of the translation pair;
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+ * `dataset` - The name of the original dataset from which the document was sourced.
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+
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+ ## Bias, Risks, and Limitations
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+
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+ DocBlocks may reflect linguistic, cultural, and domain biases from its source corpora, and its performance is influenced by language coverage and document structure variability.
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+
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+
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+ ## Citation
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+ ```bibtex
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+ @misc{multilingual_contextualization_llm_2025,
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+ title={Multilingual Contextualization of Large Language Models for Document-Level Machine Translation},
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+ author={Miguel Moura Ramos and Patrick Fernandes and Sweta Agrawal and André F. T. Martins},
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+ year={2025},
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+ eprint={2504.12140},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2504.12140},
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+ }
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+ ```