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https://github.com/facebookresearch/MLDoc
A Corpus for Multilingual Document Classification in Eight Languages.
https://github.com/facebookresearch/MLDoc
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A Corpus for Multilingual Document Classification in Eight Languages.
- Host: GitHub
- URL: https://github.com/facebookresearch/MLDoc
- Owner: facebookresearch
- License: other
- Archived: true
- Created: 2017-12-21T16:26:45.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2022-06-06T09:20:32.000Z (over 2 years ago)
- Last Synced: 2024-04-20T00:36:52.815Z (7 months ago)
- Language: Python
- Size: 1.71 MB
- Stars: 153
- Watchers: 7
- Forks: 13
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MLDoc: A Corpus for Multilingual Document Classification in Eight Languages
This script generates a subset of the Reuters corpus with balanced class priors for eight languages. By adding Italian, Russian, Japanese and Chinese, we cover languages which are very different with respect to syntax, morphology, etc.
> Cross-lingual document classification aims at training a document classifier on resources in one language and transferring it to a different language without any additional resources. Several approaches have been proposed in the literature and the current best practice is to evaluate them on a subset of the Reuters Corpus Volume 2. However, this subset covers only few languages (English, German, French and Spanish) and almost all published works focus on the the transfer between English and German. In addition, we have observed that the class prior distributions differ significantly between the languages. We argue that this complicates the evaluation of the multilinguality.
> In this paper, we propose a new subset of the Reuters corpus with balanced class priors for eight languages. By adding Italian, Russian, Japanese and Chinese, we cover languages which are very different with respect to syntax, morphology, etc. We provide strong baselines for all language transfer directions using multilingual word and sentence embeddings respectively. Our goal is to offer a freely available framework to evaluate cross-lingual document classification, and we hope to foster by these means, research in this important area.
# Citation
If you use this dataset and/or script in your paper, please cite our LREC [paper](http://www.lrec-conf.org/proceedings/lrec2018/summaries/658.html):
```
@InProceedings{SCHWENK18.658,
author = {Holger Schwenk and Xian Li},
title = {A Corpus for Multilingual Document Classification in Eight Languages},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {may},
date = {7-12},
location = {Miyazaki, Japan},
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
publisher = {European Language Resources Association (ELRA)},
address = {Paris, France},
isbn = {979-10-95546-00-9},
language = {english}
}
```
# Benchmark Dataset (RCV1/RCV2 indices)
Access to the actual news stories of the Reuters Corpus (both RCV1 and RCV2) requires a NIST agreement (see http://trec.nist.gov/data/reuters/reuters.html for details). So we created indices to the sample documents included in the benchmark dataset. They are organized by language, and each language has a .train, .dev and .test file respectively. Each row in those files contains
```
```
where `document_index` is a concatenation of sub-corpus name and document name, e.g. index FDCH5-39373 corresponds to text extracted between the `
` and `
` in ``/FDCH5/39373.xml.
To use these indices to run the exact same benchmarking, the following script can be used to generate the actual documents and their labels:
```
python generate_documents.py \
--indices-file \
--output-filename \
--rcv-dir
```
See our paper for benchmark results on this dataset.
# Evaluate variance through bootstrapping
The sampling script generates samples with uniform class priors:
```
python sampling_rcv2.py --input-dir --output-dir
```
Run it with different seeds to generate different train/dev/test samples to evaluate classification results variance.
## Requirements and Installation
- A computer running macOS or Linux
- Python version >= 0.3.0.