Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining

Paper

Implementation of the paper of Chih-chan Tien and Shane Steinert-Threlkeld. 2022. ‘Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining’.

Libraries

Python libraries listed in requirements.txt are used.

Data

Training corpora

The bilingual corpora may be downloaded through scripts in the repository of XLM, and then moved to data/xlm/para. For example, the command below can be used to get parallel texts in English and German with scripts from the the repository of XLM.

# Use the script `get-data-para.sh` of XLM
./get-data-para.sh de-en

Evaluation datasets

The evaluation dataset may be downloaded with scripts/get_data.sh.

./get_data.sh

Training and evaluation

The shell commands below can be used to replicate the main results in Table 2 and Table 3.

Unsupervised model (with adversarial and cycle losses)

python interlens pipeline \
    --param_path "aligner/gancycle_aligner.jsonnet" \
    --lens "softmaxlinear pass boe linear" \
    --criterion_variants "triplet_ranking" \
    --margins "0.2" \
    --nums_random_samples "1" \
    --batch_size "32" --epoch_size "524288" \
    --critic_criterion "max_difference" \
    --cycle_loss_lambda "10" \
    --critic_num_steps "2" \
    --pivot_languages "de"

Bilingually-supervised model

python interlens pipeline \
    --param_path "aligner/para_aligner.jsonnet" \
    --lens "softmaxlinear pass boe linear" \
    --criterion_variants "triplet_ranking" \
    --margins "0.0" \
    --nums_random_samples "0" \
    --batch_size "32" --epoch_size "524288" \
    --pivot_languages "de"