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# Basic Information
This is the Dr. Decr-large model used in XOR-TyDi leaderboard task 1 whitebox submission.
https://nlp.cs.washington.edu/xorqa/
The detailed implementation of the model can be found in:
https://arxiv.org/pdf/2112.08185.pdf
Source code to train the model can be found via PrimeQA's IR component:
https://github.com/primeqa/primeqa/tree/main/examples/drdecr
It is a Neural IR model built on top of the ColBERTv2 api and not directly compatible with Huggingface API. The inference result on XOR Dev dataset is:
```
R@2kt R@5kt
ko 69.1 75.1
ar 68.0 75.7
bn 81.9 85.2
fi 68.2 73.6
ru 67.1 72.2
ja 63.1 69.7
te 82.8 86.1
Avg 71.4 76.8
```
# Limitations and Bias
This model used pre-trained XLMR-large model and fine tuned on 7 languages in XOR-TyDi leaderboard. The performance of other languages was not tested.
Since the model was fine-tuned on a large pre-trained language model XLM-Roberta, biases associated with the pre-existing XLM-Roberta model may be present in our fine-tuned model, Dr. Decr
# Citation
```
@article{Li2021_DrDecr,
doi = {10.48550/ARXIV.2112.08185},
url = {https://arxiv.org/abs/2112.08185},
author = {Li, Yulong and Franz, Martin and Sultan, Md Arafat and Iyer, Bhavani and Lee, Young-Suk and Sil, Avirup},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Learning Cross-Lingual IR from an English Retriever},
publisher = {arXiv},
year = {2021}
}
```
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