Word to word translation dictionaries

Словосочетания

Автоматический перевод

слово в слово, слово за слово

Перевод по словам

word  — слово, известие, речь, обещание, текст, вести, сформулировать, выражать словами
word  — слово, известие, речь, обещание, текст, вести, сформулировать, выражать словами

Примеры

Easy-to-use word translations for 3,564 language pairs.

This is the official code accompanying our LREC 2020 paper.

Summary

• A large collection of freely & publicly available bilingual lexicons
  for 3,564 language pairs across 62 unique languages.
• Easy-to-use Python interface for accessing top-k word translations and
  for building a new bilingual lexicon from a custom parallel corpus.
• Constructed using a simple approach that yields bilingual lexicons with
  high coverage and competitive translation quality.

Usage

First, install the package using pip:

pip install word2word

OR

git clone https://github.com/kakaobrain/word2word
python setup.py install

Then, in Python, download the model and retrieve top-5 word translations
of any given word to the desired language:

from word2word import Word2word
en2fr = Word2word("en", "fr")
print(en2fr("apple"))
# out: ['pomme', 'pommes', 'pommier', 'tartes', 'fleurs']

Supported Languages

We provide top-k word-to-word translations across all available pairs
from OpenSubtitles2018.
This amounts to a total of 3,564 language pairs across 62 unique languages.

The full list is provided here.

Methodology

Our approach computes top-k word translations based on
the co-occurrence statistics between cross-lingual word pairs in a parallel corpus.
We additionally introduce a correction term that controls for any confounding effect
coming from other source words within the same sentence.
The resulting method is an efficient and scalable approach that allows us to
construct large bilingual dictionaries from any given parallel corpus.

For more details, see the Methodology section of our paper.

Building a Bilingual Lexicon on a Custom Parallel Corpus

The word2word package also provides interface for
building a custom bilingual lexicon using a different parallel corpus.
Here, we show an example of building one from
the Medline English-French dataset:

from word2word import Word2word

# custom parallel data: data/pubmed.en-fr.en, data/pubmed.en-fr.fr
my_en2fr = Word2word.make("en", "fr", "data/pubmed.en-fr")
# ...building...
print(my_en2fr("mitochondrial"))
# out: ['mitochondriale', 'mitochondriales', 'mitochondrial', 
#       'cytopathies', 'mitochondriaux']

When built from source, the bilingual lexicon can also be constructed from the command line as follows:

python make.py --lang1 en --lang2 fr --datapref data/pubmed.en-fr

In both cases, the custom lexicon (saved to datapref/ by default) can be re-loaded in Python:

from word2word import Word2word
my_en2fr = Word2word.load("en", "fr", "data/pubmed.en-fr")
# Loaded word2word custom bilingual lexicon from data/pubmed.en-fr/en-fr.pkl

Multiprocessing

In both the Python interface and the command line interface,
make uses multiprocessing with 16 CPUs by default.
The number of CPU workers can be adjusted by setting
num_workers=N (Python) or --num_workers N (command line).

References

If you use word2word for research, please cite our paper:

@inproceedings{choe2020word2word,
 author = {Yo Joong Choe and Kyubyong Park and Dongwoo Kim},
 title = {word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs},
 booktitle = {Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)},
 year = {2020}
}

All of our pre-computed bilingual lexicons were constructed from the publicly available
OpenSubtitles2018 dataset:

@inproceedings{lison-etal-2018-opensubtitles2018,
    title = "{O}pen{S}ubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora",
    author = {Lison, Pierre  and
      Tiedemann, J{"o}rg  and
      Kouylekov, Milen},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1275",
}

Authors

Kyubyong Park,
Dongwoo Kim, and
YJ Choe

English definitions

Choose from corpus-informed dictionaries for English language learners at all levels. They’re ideal for anyone preparing for Cambridge English exams and IELTS. The English dictionary includes the Cambridge Advanced Learner’s Dictionary, the Cambridge Academic Content Dictionary, and the Cambridge Business English Dictionary. The Cambridge Learner’s Dictionary is perfect for intermediate learners.