Papers › BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
Haoran Xu, Benjamin Van Durme, Kenton Murray
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 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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | IWSLT2014 German-English | BiBERT | BLEU score | 38.61 | #2 of 34 | Archive leaderboard | report |
| Machine Translation | IWSLT2014 German-English | BiBERT | Number of Params | 73.8M | #2 of 34 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-German | BiBERT | BLEU score | 31.26 | #5 of 91 | Archive leaderboard | report |
| Machine Translation | WMT2014 German-English | BiBERT | BLEU score | 34.94 | #2 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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