Author:
Rudra Koustav,Fernando Zeon Trevor,Anand Avishek
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Information Systems
Reference84 articles.
1. Ahmad, A., Constant, N., Yang, Y., & Cer, D. (2019). Reqa: An evaluation for end-to-end answer retrieval models. arXiv:1907.04780
2. Anand, A., Leonhardt, J., Singh, J., Rudra, K., & Anand, A. (2023). Data augmentation for sample efficient and robust document ranking.
3. Chang, W.-C., Yu, F. X., Chang, Y.-W., Yang, Y., & Kumar, S. (2020). Pre-training tasks for embedding-based large-scale retrieval. arXiv:2002.03932
4. Choi, E., Lee, S., Choi, M., Ko, H., Song, Y.-I., & Lee, J. (2022). Spade: Improving sparse representations using a dual document encoder for first-stage retrieval. In Proceedings of the 31st ACM international conference on information and knowledge management, CIKM ’22 (pp. 272-282).
5. Cohen, N., Portnoy, A., Fetahu, B., & Ingber, A. (2022). SDR: Efficient neural re-ranking using succinct document representation. In Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers), (pp. 6624–6637). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.457, https://aclanthology.org/2022.acl-long.457