Document (context) encoder, trained on monolingual (English queries) version of the XOR-TyDi training data, as described in the XOR-TyDi task description https://nlp.cs.washington.edu/xorqa/.
Usage
The model is compatible with the PrimeQA DPR engine, or the Hugging Face DPR engine as shown this example https://huggingface.co./docs/datasets/faiss_es.
The model uses the tokenizer from facebook/dpr-question_encoder-multiset-base.
Performance comparison
| R@5kt | R@2kt | model |
|---|---|---|
| 69.6 | 62.2 | DPR, En, XOR-TyDi paper (https://arxiv.org/pdf/2010.11856.pdf, table 13) |
| 70.22 | 64.34 | DPR, En, trained on En (human) version of XOR |
BibTeX entry and citation info
@inproceedings{asai-etal-2021-xor,
title = "{XOR} {QA}: Cross-lingual Open-Retrieval Question Answering",
author = "Asai, Akari and
Kasai, Jungo and
Clark, Jonathan and
Lee, Kenton and
Choi, Eunsol and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.46",
doi = "10.18653/v1/2021.naacl-main.46",
pages = "547--564",
}
@inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and
Oguz, Barlas and
Min, Sewon and
Lewis, Patrick and
Wu, Ledell and
Edunov, Sergey and
Chen, Danqi and
Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.550",
doi = "10.18653/v1/2020.emnlp-main.550",
pages = "6769--6781",
}
- Downloads last month
- 3