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LASER Language-Agnostic SEntence Representations

LASER is a library to calculate and use multilingual sentence embeddings.

NEWS

CURRENT VERSION:

According to our experience, the sentence encoder also supports code-switching, i.e. the same sentences can contain words in several different languages.

We have also some evidence that the encoder can generalize to other languages which have not been seen during training, but which are in a language family which is covered by other languages.

A detailed description of how the multilingual sentence embeddings are trained can be found here, together with an experimental evaluation.

The core sentence embedding package: laser_encoders

We provide a package laser_encoders with minimal dependencies. It supports LASER-2 (a single encoder for the languages listed below) and LASER-3 (147 language-specific encoders described here).

The package can be installed simply with pip install laser_encoders and used as below:

from laser_encoders import LaserEncoderPipeline
encoder = LaserEncoderPipeline(lang="eng_Latn")
embeddings = encoder.encode_sentences(["Hi!", "This is a sentence encoder."])
print(embeddings.shape)  # (2, 1024)

The laser_encoders readme file provides more examples of its installation and usage.

The full LASER kit

Apart from the laser_encoders, we provide support for LASER-1 (the original multilingual encoder) and for various LASER applications listed below.

Dependencies

Installation

Applications

We showcase several applications of multilingual sentence embeddings with code to reproduce our results (in the directory "tasks").

For all tasks, we use exactly the same multilingual encoder, without any task specific optimization or fine-tuning.

License

LASER is BSD-licensed, as found in the LICENSE file in the root directory of this source tree.

Supported languages

The original LASER model was trained on the following languages:

Afrikaans, Albanian, Amharic, Arabic, Armenian, Aymara, Azerbaijani, Basque, Belarusian, Bengali, Berber languages, Bosnian, Breton, Bulgarian, Burmese, Catalan, Central/Kadazan Dusun, Central Khmer, Chavacano, Chinese, Coastal Kadazan, Cornish, Croatian, Czech, Danish, Dutch, Eastern Mari, English, Esperanto, Estonian, Finnish, French, Galician, Georgian, German, Greek, Hausa, Hebrew, Hindi, Hungarian, Icelandic, Ido, Indonesian, Interlingua, Interlingue, Irish, Italian, Japanese, Kabyle, Kazakh, Korean, Kurdish, Latvian, Latin, Lingua Franca Nova, Lithuanian, Low German/Saxon, Macedonian, Malagasy, Malay, Malayalam, Maldivian (Divehi), Marathi, Norwegian (Bokmål), Occitan, Persian (Farsi), Polish, Portuguese, Romanian, Russian, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Swahili, Swedish, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Turkish, Uighur, Ukrainian, Urdu, Uzbek, Vietnamese, Wu Chinese and Yue Chinese.

We have also observed that the model seems to generalize well to other (minority) languages or dialects, e.g.

Asturian, Egyptian Arabic, Faroese, Kashubian, North Moluccan Malay, Nynorsk Norwegian, Piedmontese, Sorbian, Swabian, Swiss German or Western Frisian.

LASER3

Updated LASER models referred to as LASER3 supplement the above list with support for 147 languages. The full list of supported languages can be seen here.

References

[1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017

[2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018.

[3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018

[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018.

[5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018.

[6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018.

[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019.

[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining,, NeurIPS 2021, pages 15748-15761.

[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages