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| # LASER: application to cross-lingual document classification | |
| This codes shows how to use the multilingual sentence embedding for | |
| cross-lingual document classification, using the MLDoc corpus [1]. | |
| We train a document classifier on one language (e.g. English) and apply it then | |
| to several other languages without using any resource of that language | |
| (e.g. German, Spanish, French, Italian, Japanese, Russian and Chinese) | |
| ## Installation | |
| * Please first download the MLDoc corpus from | |
| [here](https://github.com/facebookresearch/MLDoc) | |
| and install it in the directory MLDoc | |
| * Calculate the multilingual sentence embeddings for all languages | |
| and train the classifier `bash ./mldoc.sh` | |
| ## Results | |
| We use an MLP classifier with two hidden layers and Adam optimization. | |
| You should get the following results for zero-short cross-lingual transfer | |
| These results are in average better than those reported in [2] since the system has | |
| been improved since publication. | |
| | Train language | En | De | Es | Fr | It | Ja | Ru | Zh | | |
| |----------------|--------|--------|--------|--------|--------|--------|--------|-------| | |
| | English (en) | 90.73 | 86.25 | 79.30 | 78.03 | 70.20 | 60.95 | 67.25 | 70.98 | | |
| | German (de) | 80.75 | 92.70 | 79.60 | 82.83 | 73.25 | 56.80 | 68.18 | 72.90 | | |
| | Spanish (es) | 69.58 | 79.73 | 88.75 | 75.30 | 71.10 | 59.65 | 59.83 | 61.70 | | |
| | French (fr) | 80.08 | 87.03 | 78.40 | 90.80 | 71.08 | 53.60 | 67.55 | 66.12 | | |
| | Italian (it) | 74.15 | 80.73 | 82.60 | 78.35 | 85.93 | 55.15 | 68.83 | 56.10 | | |
| | Japanese (ja) | 68.45 | 81.90 | 67.95 | 67.95 | 57.98 | 85.15 | 53.70 | 66.12 | | |
| | Russian (ru) | 72.60 | 79.62 | 68.18 | 71.28 | 67.00 | 59.23 | 84.65 | 65.62 | | |
| | Chinese (zh) | 77.95 | 83.38 | 78.38 | 75.83 | 70.33 | 55.25 | 66.62 | 88.98 | | |
| All numbers are accuracies on the test set. | |
| ## References | |
| Details on the corpus are described in this paper: | |
| [1] Holger Schwenk and Xian Li, | |
| [*A Corpus for Multilingual Document Classification in Eight Languages*](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf), | |
| LREC, pages 3548-3551, 2018. | |
| Detailed system description: | |
| [2] Mikel Artetxe and Holger Schwenk, | |
| [*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464), | |
| arXiv, Dec 26 2018. | |