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# ParaDocs |
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Data availability limits the scope of any given task. |
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In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. |
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Now, despite the emergence of long-sequence methods, we remain within a sentence-level paradigm and without data to adequately approach context-aware machine translation. |
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Most large-scale datasets have been processed through a pipeline that discards document-level metadata. |
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[ParaDocs](https://aclanthology.org/2024.findings-acl.589/) is a publicly available dataset that produces parallel annotations for the document-level metadata of three large publicly available corpora (ParaCrawl, Europal, and News Commentary) in many languages. |
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Using this data and the following scripts, you can download parallel document contexts for the purpose of training context-aware machine translation systems. |
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If you have questions about this data or use of the following scripts, please do not hesitate to contact the maintainer at rewicks@jhu.edu. |
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# Quick Start |
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The scripts to download and process the data can be found [here](https://github.com/rewicks/ParaDocs/): |
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Clone these scripts: |
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``` |
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git clone https://github.com/rewicks/ParaDocs.git |
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``` |
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From this directory, you can stream a specific language and split from huggingface with: |
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``` |
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paradocs/paradocs-hf --name en-de-strict --minimum_size 2 --frequency_cutoff 100 --lid_cutoff 0.5 |
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``` |
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It may alternatively be faster to download the `*.gz` files of your desired split and then pipe them through the `paradocs/paradocs` file for filtering. |
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``` |
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zcat data/en-de/strict* | paradocs/paradocs --minimum_size 2 --frequency_cutoff 100 --lid_cutoff 0.5 |
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``` |
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The filtering commandline arguments are explained in more detial in Section 3.2. |
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## The Paper |
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If you use this dataset in your research. Please cite our [paper](https://aclanthology.org/2024.findings-acl.589/). |
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--- |
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license: apache-2.0 |
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--- |
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