This is the repository for our EMNLP 2021 paper "BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation".

@inproceedings{xu-etal-2021-bert,
    title = "{BERT}, m{BERT}, or {B}i{BERT}? A Study on Contextualized Embeddings for Neural Machine Translation",
    author = "Xu, Haoran  and
      Van Durme, Benjamin  and
      Murray, Kenton",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.534",
    pages = "6663--6675",
    abstract = "The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, proposed methods for incorporating pre-trained models are non-trivial and mainly focus on BERT, which lacks a comparison of the impact that other pre-trained models may have on translation performance. In this paper, we demonstrate that simply using the output (contextualized embeddings) of a tailored and suitable bilingual pre-trained language model (dubbed BiBERT) as the input of the NMT encoder achieves state-of-the-art translation performance. Moreover, we also propose a stochastic layer selection approach and a concept of a dual-directional translation model to ensure the sufficient utilization of contextualized embeddings. In the case of without using back translation, our best models achieve BLEU scores of 30.45 for En→De and 38.61 for De→En on the IWSLT{'}14 dataset, and 31.26 for En→De and 34.94 for De→En on the WMT{'}14 dataset, which exceeds all published numbers.",
}

Prerequisites

conda create -n bibert python=3.7
conda activate bibert

• transformers >= 4.4.2

  pip install transformers
  

• Install our fairseq repo

  cd BiBERT
  pip install --editable ./
  

• hydra = 1.0.3

  pip install hydra-core==1.0.3
  

BiBERT

Download our pre-trained bilingual English-German BiBERT:

from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("jhu-clsp/bibert-ende")
model = AutoModel.from_pretrained("jhu-clsp/bibert-ende")

An example of obtaining the contextual embeddings of the input sentence:

import torch
text = "Hello world!"

## Load model and tokenizer
tokenizer = BertTokenizer.from_pretrained("jhu-clsp/bibert-ende")
model = AutoModel.from_pretrained("jhu-clsp/bibert-ende")

## Feed input sentence to the model
tokens = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
input_ids = torch.tensor(tokens, dtype=torch.long).unsqueeze(0)

## Obtain the contextual embeddings of BiBERT
layer = -1 # Last layer
output = model(input_ids, output_hidden_states=True)[2][layer]

Reproduction

Preprocessing

Download and prepare IWSLT'14 dataset (if you meet warnings like file config.json not found, please feel safe to ignore it):

cd download_prepare
bash download_and_prepare_data.sh

After download and preprocessing, three preprocessed data bin will be shown in download_prepare folder:

• data: de->en preprocessed data for ordinary one-way translation
• data_mixed: dual-directional translation data
• data_mixed_ft: after dual-directional training, fine-tuning on one-way translation data

Training

Train a model for one-way translation. Note that passing field --use_drop_embedding to consider number of layers in stochastic layer selection, where the default is 8. Training with less GPUs should increase --update-freq, e.g., update-freq=8 for 2 GPUs and update-freq=4 for 4 GPUs.

bash train.sh

Train a model for dual-directional translation and further fine-tuning:

bash train-dual.sh

Evaluation

Translation for one-way model:

bash generate.sh

Translation for dual-directional model:

bash generate-dual.sh

The BLEU score will be printed out in the final output after running generate.sh.

WMT'14 Data Training

Download our preprocessed WMT'14 dataset wmt-data.zip

cd download_prepare
unzip wmt-data.zip

The resource of training data comes from Standford WMT'14 dataset. The data in wmt-data has been preprocessed the same way as IWSLT'14.

Similar to IWSLT'14 training and evaluation discribed above, we train and evaluate the model by running train-wmt.sh/train-wmt-dual.sh and generate-wmt.sh/generate-wmt-dual.sh.