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Disclaimer :- I don't own the weights of ernie-m-large neither did I train the model. I only converted the model weights from paddle to pytorch(using the scripts listed in files).

The real(paddle) weights can be found here.

The rest of the README is copied from the same page listed above,

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PaddlePaddle/ernie-m-base

Ernie-M

ERNIE-M, proposed by Baidu, is a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. The insight is to integrate back-translation into the pre-training process by generating pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks.

We proposed two novel methods to align the representation of multiple languages:

Cross-Attention Masked Language Modeling(CAMLM): In CAMLM, we learn the multilingual semantic representation by restoring the MASK tokens in the input sentences. Back-Translation masked language modeling(BTMLM): We use BTMLM to train our model to generate pseudo-parallel sentences from the monolingual sentences. The generated pairs are then used as the input of the model to further align the cross-lingual semantics, thus enhancing the multilingual representation.

ernie-m

Benchmark

XNLI

XNLI is a subset of MNLI and has been translated into 14 different kinds of languages including some low-resource languages. The goal of the task is to predict testual entailment (whether sentence A implies / contradicts / neither sentence B).

Model en fr es de el bg ru tr ar vi th zh hi sw ur Avg
Cross-lingual Transfer
XLM 85.0 78.7 78.9 77.8 76.6 77.4 75.3 72.5 73.1 76.1 73.2 76.5 69.6 68.4 67.3 75.1
Unicoder 85.1 79.0 79.4 77.8 77.2 77.2 76.3 72.8 73.5 76.4 73.6 76.2 69.4 69.7 66.7 75.4
XLM-R 85.8 79.7 80.7 78.7 77.5 79.6 78.1 74.2 73.8 76.5 74.6 76.7 72.4 66.5 68.3 76.2
INFOXLM 86.4 80.6 80.8 78.9 77.8 78.9 77.6 75.6 74.0 77.0 73.7 76.7 72.0 66.4 67.1 76.2
ERNIE-M 85.5 80.1 81.2 79.2 79.1 80.4 78.1 76.8 76.3 78.3 75.8 77.4 72.9 69.5 68.8 77.3
XLM-R Large 89.1 84.1 85.1 83.9 82.9 84.0 81.2 79.6 79.8 80.8 78.1 80.2 76.9 73.9 73.8 80.9
INFOXLM Large 89.7 84.5 85.5 84.1 83.4 84.2 81.3 80.9 80.4 80.8 78.9 80.9 77.9 74.8 73.7 81.4
VECO Large 88.2 79.2 83.1 82.9 81.2 84.2 82.8 76.2 80.3 74.3 77.0 78.4 71.3 80.4 79.1 79.9
ERNIR-M Large 89.3 85.1 85.7 84.4 83.7 84.5 82.0 81.2 81.2 81.9 79.2 81.0 78.6 76.2 75.4 82.0
Translate-Train-All
XLM 85.0 80.8 81.3 80.3 79.1 80.9 78.3 75.6 77.6 78.5 76.0 79.5 72.9 72.8 68.5 77.8
Unicoder 85.6 81.1 82.3 80.9 79.5 81.4 79.7 76.8 78.2 77.9 77.1 80.5 73.4 73.8 69.6 78.5
XLM-R 85.4 81.4 82.2 80.3 80.4 81.3 79.7 78.6 77.3 79.7 77.9 80.2 76.1 73.1 73.0 79.1
INFOXLM 86.1 82.0 82.8 81.8 80.9 82.0 80.2 79.0 78.8 80.5 78.3 80.5 77.4 73.0 71.6 79.7
ERNIE-M 86.2 82.5 83.8 82.6 82.4 83.4 80.2 80.6 80.5 81.1 79.2 80.5 77.7 75.0 73.3 80.6
XLM-R Large 89.1 85.1 86.6 85.7 85.3 85.9 83.5 83.2 83.1 83.7 81.5 83.7 81.6 78.0 78.1 83.6
VECO Large 88.9 82.4 86.0 84.7 85.3 86.2 85.8 80.1 83.0 77.2 80.9 82.8 75.3 83.1 83.0 83.0
ERNIE-M Large 89.5 86.5 86.9 86.1 86.0 86.8 84.1 83.8 84.1 84.5 82.1 83.5 81.1 79.4 77.9 84.2

Cross-lingual Named Entity Recognition

  • datasets:CoNLI
Model en nl es de Avg
Fine-tune on English dataset
mBERT 91.97 77.57 74.96 69.56 78.52
XLM-R 92.25 78.08 76.53 69.60 79.11
ERNIE-M 92.78 78.01 79.37 68.08 79.56
XLM-R LARGE 92.92 80.80 78.64 71.40 80.94
ERNIE-M LARGE 93.28 81.45 78.83 72.99 81.64
Fine-tune on all dataset
XLM-R 91.08 89.09 87.28 83.17 87.66
ERNIE-M 93.04 91.73 88.33 84.20 89.32
XLM-R LARGE 92.00 91.60 89.52 84.60 89.43
ERNIE-M LARGE 94.01 93.81 89.23 86.20 90.81

Cross-lingual Question Answering

  • datasets:MLQA
Model en es de ar hi vi zh Avg
mBERT 77.7 / 65.2 64.3 / 46.6 57.9 / 44.3 45.7 / 29.8 43.8 / 29.7 57.1 / 38.6 57.5 / 37.3 57.7 / 41.6
XLM 74.9 / 62.4 68.0 / 49.8 62.2 / 47.6 54.8 / 36.3 48.8 / 27.3 61.4 / 41.8 61.1 / 39.6 61.6 / 43.5
XLM-R 77.1 / 64.6 67.4 / 49.6 60.9 / 46.7 54.9 / 36.6 59.4 / 42.9 64.5 / 44.7 61.8 / 39.3 63.7 / 46.3
INFOXLM 81.3 / 68.2 69.9 / 51.9 64.2 / 49.6 60.1 / 40.9 65.0 / 47.5 70.0 / 48.6 64.7 / 41.2 67.9 / 49.7
ERNIE-M 81.6 / 68.5 70.9 / 52.6 65.8 / 50.7 61.8 / 41.9 65.4 / 47.5 70.0 / 49.2 65.6 / 41.0 68.7 / 50.2
XLM-R LARGE 80.6 / 67.8 74.1 / 56.0 68.5 / 53.6 63.1 / 43.5 62.9 / 51.6 71.3 / 50.9 68.0 / 45.4 70.7 / 52.7
INFOXLM LARGE 84.5 / 71.6 75.1 / 57.3 71.2 / 56.2 67.6 / 47.6 72.5 / 54.2 75.2 / 54.1 69.2 / 45.4 73.6 / 55.2
ERNIE-M LARGE 84.4 / 71.5 74.8 / 56.6 70.8 / 55.9 67.4 / 47.2 72.6 / 54.7 75.0 / 53.7 71.1 / 47.5 73.7 / 55.3

Cross-lingual Paraphrase Identification

  • datasets:PAWS-X
Model en de es fr ja ko zh Avg
Cross-lingual Transfer
mBERT 94.0 85.7 87.4 87.0 73.0 69.6 77.0 81.9
XLM 94.0 85.9 88.3 87.4 69.3 64.8 76.5 80.9
MMTE 93.1 85.1 87.2 86.9 72.0 69.2 75.9 81.3
XLM-R LARGE 94.7 89.7 90.1 90.4 78.7 79.0 82.3 86.4
VECO LARGE 96.2 91.3 91.4 92.0 81.8 82.9 85.1 88.7
ERNIE-M LARGE 96.0 91.9 91.4 92.2 83.9 84.5 86.9 89.5
Translate-Train-All
VECO LARGE 96.4 93.0 93.0 93.5 87.2 86.8 87.9 91.1
ERNIE-M LARGE 96.5 93.5 93.3 93.8 87.9 88.4 89.2 91.8

Cross-lingual Sentence Retrieval

  • dataset:Tatoeba
Model Avg
XLM-R LARGE 75.2
VECO LARGE 86.9
ERNIE-M LARGE 87.9
ERNIE-M LARGE( after fine-tuning) 93.3

Citation Info

@article{Ouyang2021ERNIEMEM,
  title={ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora},
  author={Xuan Ouyang and Shuohuan Wang and Chao Pang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang},
  journal={ArXiv},
  year={2021},
  volume={abs/2012.15674}
}
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