Unsupervised Translation Direction Detection

This repository contains the code for the paper "Machine Translation Models are Zero-Shot Detectors of Translation Direction".

Paper: http://arxiv.org/abs/2401.06769
Demo: https://huggingface.co/spaces/ZurichNLP/translation-direction-detection

Installation

• Requirements: Python >= 3.11, PyTorch
• Clone this repository
• pip install .

Usage

Analyzing individual sentences

import translation_direction_detection as tdd

detector = tdd.TranslationDirectionDetector()

sentence1 = "Können Sie mir dabei weiter helfen?"
sentence2 = "Pouvez-vous m'aider ?"
lang1 = "de"
lang2 = "fr"

result = detector.detect(sentence1, sentence2, lang1, lang2)
print(result)
# Predicted direction: de→fr
# 1 sentence pair
# de→fr: 0.554
# fr→de: 0.446

Note: TranslationDirectionDetector loads the model https://huggingface.co/alirezamsh/small100 by default. Pass an nmtscore.NMTScorer object to the constructor to use a different model.

Analyzing documents (= lists of aligned sentences)

import translation_direction_detection as tdd

detector = tdd.TranslationDirectionDetector()

sentences1 = ["Können Sie mir dabei weiter helfen?", "Ja; sehr gerne!"]
sentences2 = ["Pouvez-vous m'aider ?", "Oui, avec plaisir !"]
lang1 = "de"
lang2 = "fr"

result = detector.detect(sentences1, sentences2, lang1, lang2)
print(result)
# Predicted direction: de→fr
# 2 sentence pairs
# de→fr: 0.564
# fr→de: 0.436

# With hypothesis testing (permutation test)
result = detector.detect(sentences1, sentences2, lang1, lang2, return_pvalue=True)
print(result)
# Predicted direction: de→fr
# ...
# p-value: 0.5

Reproducing the experiments from the paper

See experiments/README.md.

License

MIT License

Citation

@misc{wastl2024machine,
      title={Machine Translation Models are Zero-Shot Detectors of Translation Direction}, 
      author={Michelle Wastl and Jannis Vamvas and Rico Sennrich},
      year={2024},
      eprint={2401.06769},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}