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# Basic Information
This is the Dr. Decr 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/updated-documentation-readme/primeqa/ir/dense/colbert_top
It is a Neural IR model built on top of the ColBERTv1 api and not directly compatible with Huggingface API. The inference result on XOR Dev dataset is:
```
R@2kt R@5kt
te 79.41 83.19
bn 77.96 82.89
fi 65.92 72.61
ja 63.07 67.63
ko 60.35 68.07
ru 60.76 68.35
ar 65.70 73.14
Avg 67.60 73.70
```
# Limitations and Bias
This model used pre-trained XLMR 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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