Update README.md

README.md

@@ -5,17 +5,24 @@ language:
 ---
 Our bibert-ende is a bilingual English-German Language Model. Please check out our EMNLP 2021 paper "[BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation](https://arxiv.org/abs/2109.04588)" for more details.
 ```
-@misc{xu2021bert,
-      title={BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation}, 
-      author={Haoran Xu and Benjamin Van Durme and Kenton Murray},
-      year={2021},
-      eprint={2109.04588},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+@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.",
 }
 ```
 # Download
 
+Note that tokenizer package is `BertTokenizer` not `AutoTokenizer`.
 ```
 from transformers import BertTokenizer, AutoModel
 tokenizer = BertTokenizer.from_pretrained("jhu-clsp/bibert-ende")