# 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}
}
```