awesome-align 0.1.7

An awesome word alignment tool

AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders

awesome-align is a tool that can extract word alignments from multilingual BERT (mBERT) [Demo] and allows you to fine-tune mBERT on parallel corpora for better alignment quality (see our paper for more details).

Dependencies

First, you need to install the dependencies:

pip install -r requirements.txt
python setup.py install

Input format

Inputs should be tokenized and each line is a source language sentence and its target language translation, separated by (|||). You can see some examples in the examples folder.

Extracting alignments

Here is an example of extracting word alignments from multilingual BERT:

DATA_FILE=/path/to/data/file
MODEL_NAME_OR_PATH=bert-base-multilingual-cased
OUTPUT_FILE=/path/to/output/file

CUDA_VISIBLE_DEVICES=0 awesome-align \
    --output_file=$OUTPUT_FILE \
    --model_name_or_path=$MODEL_NAME_OR_PATH \
    --data_file=$DATA_FILE \
    --extraction 'softmax' \
    --batch_size 32

This produces outputs in the i-j Pharaoh format. A pair i-j indicates that the ith word (zero-indexed) of the source sentence is aligned to the jth word of the target sentence.

You can set --output_prob_file if you want to obtain the alignment probability and set --output_word_file if you want to obtain the aligned word pairs (in the src_word<sep>tgt_word format). You can also set --cache_dir to specify where you want to cache multilingual BERT.

You can also set MODEL_NAME_OR_PATH to the path of your fine-tuned model as shown below.

Fine-tuning on parallel data

If there is parallel data available, you can fine-tune embedding models on that data.

Here is an example of fine-tuning mBERT that balances well between efficiency and effectiveness:

TRAIN_FILE=/path/to/train/file
EVAL_FILE=/path/to/eval/file
OUTPUT_DIR=/path/to/output/directory

CUDA_VISIBLE_DEVICES=0 awesome-train \
    --output_dir=$OUTPUT_DIR \
    --model_name_or_path=bert-base-multilingual-cased \
    --extraction 'softmax' \
    --do_train \
    --train_tlm \
    --train_so \
    --train_data_file=$TRAIN_FILE \
    --per_gpu_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --num_train_epochs 1 \
    --learning_rate 2e-5 \
    --save_steps 4000 \
    --max_steps 20000 \
    --do_eval \
    --eval_data_file=$EVAL_FILE

You can also fine-tune the model a bit longer with more training objectives for better quality:

TRAIN_FILE=/path/to/train/file
EVAL_FILE=/path/to/eval/file
OUTPUT_DIR=/path/to/output/directory

CUDA_VISIBLE_DEVICES=0 awesome-train \
    --output_dir=$OUTPUT_DIR \
    --model_name_or_path=bert-base-multilingual-cased \
    --extraction 'softmax' \
    --do_train \
    --train_mlm \
    --train_tlm \
    --train_tlm_full \
    --train_so \
    --train_psi \
    --train_data_file=$TRAIN_FILE \
    --per_gpu_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --num_train_epochs 1 \
    --learning_rate 2e-5 \
    --save_steps 10000 \
    --max_steps 40000 \
    --do_eval \
    --eval_data_file=$EVAL_FILE

If you want high alignment recalls, you can turn on the --train_co option, but note that the alignment precisions may drop. You can set --cache_dir to specify where you want to cache multilingual BERT.

Supervised settings

In supervised settings where gold word alignments are available for your training data, you can incorporate the supervised signals into our self-training objective (--train_so) and here is an example command:

TRAIN_FILE=/path/to/train/file
TRAIN_GOLD_FILE=/path/to/train/gold/file
OUTPUT_DIR=/path/to/output/directory

CUDA_VISIBLE_DEVICES=0 awesome-train \
    --output_dir=$OUTPUT_DIR \
    --model_name_or_path=bert-base-multilingual-cased \
    --extraction 'softmax' \
    --do_train \
    --train_so \
    --train_data_file=$TRAIN_FILE \
    --train_gold_file=$TRAIN_GOLD_FILE \
    --per_gpu_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --num_train_epochs 5 \
    --learning_rate 1e-4 \
    --save_steps 200

See examples/*.gold for the example format of the gold alignments. You need to turn on the --gold_one_index option if the gold alignments are 1-indexed and you can turn on the --ignore_possible_alignments option if you want to ignore possible alignments.

Model performance

The following table shows the alignment error rates (AERs) of our models and popular statistical word aligners on five language pairs. The De-En, Fr-En, Ro-En datasets can be obtained following this repo, the Ja-En data is from this link and the Zh-En data is available at this link. The best scores are in bold.

	De-En	Fr-En	Ro-En	Ja-En	Zh-En
fast_align	27.0	10.5	32.1	51.1	38.1
eflomal	22.6	8.2	25.1	47.5	28.7
Mgiza	20.6	5.9	26.4	48.0	35.1
Ours (w/o fine-tuning, softmax)	17.4	5.6	27.9	45.6	18.1
Ours (multilingually fine-tuned w/o --train_co, softmax) [Download]	15.2	4.1	22.6	37.4	13.4
Ours (multilingually fine-tuned w/ --train_co, softmax) [Download]	15.1	4.5	20.7	38.4	14.5

Citation

If you use our tool, we'd appreciate if you cite the following paper:

@inproceedings{dou2021word,
  title={Word Alignment by Fine-tuning Embeddings on Parallel Corpora},
  author={Dou, Zi-Yi and Neubig, Graham},
  booktitle={Conference of the European Chapter of the Association for Computational Linguistics (EACL)},
  year={2021}
}

Acknowledgements

Some of the code is borrowed from HuggingFace Transformers licensed under Apache 2.0 and the entmax implementation is from this repo.