Unsupervised Clustering for Negative Sampling to Optimize Open-Domain Question Answering Retrieval

Author:

Zhuang Feiqing,Zhu Conghui,Zhao Tiejun

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Chen, D., Yih, S.: ACL2020 tutorial: Open-domain Question and Answering. https://github.com/danqi/acl2020-openqa-tutorial. Accessed 5 Jul 2020

2. Juan, R.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning. vol. 242. no. 1 (2003)

3. Stephen, R., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. In: Foundations and Trends® in Information Retrieval. vol. 3. no. 4, pp. 333–389 (2009)

4. Jacob, D., et al.: BERT: Pre-training of deep bidirectional transformers for language understanding (2018). arXiv preprint arXiv:1810.04805

5. Yinhan, L., et al.: RoBERTa: A robustly optimized BERT pretraining approach (2019). arXiv preprint arXiv:1907.11692

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