Fetching the paper…
Reading the bibliography…
Pre-trained Transformers (\eg BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-training tasks for further improving the quality of dense vectors.
1904
Earlier work this paper cites.
2003
Earlier work this paper cites.
Ramos, J., et al.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the first instructional conference on machine learning. vol. 242, pp. 29–48 (2003)
2003
Earlier work this paper cites.
Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., Deng, L.: MS MARCO: A human generated machine reading comprehension dataset. In: Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016. vol. 1773 (2016), http://ceur-ws.org/Vol-1773/CoCoNIPS\_2016\_paper9.pdf
2016
Earlier work this paper cites.
Yang, P., Fang, H., Lin, J.: Anserini: Enabling the use of lucene for information retrieval research. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017. pp. 1253–1256 (2017), https://doi.org/10.1145/3077136.3080721
2017
Earlier work this paper cites.
Dai, Z., Callan, J.: Deeper text understanding for IR with contextual neural language modeling. In: Proceedings of SIGIR 2019. pp. 985–988 (2019), https://doi.org/10.1145/3331184.3331303
2019
Earlier work this paper cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL 2019. pp. 4171–4186 (2019), https://aclanthology.org/N19-1423
2019
Earlier work this paper cites.
Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with gpus. IEEE Transaction’s on Big Data 7
2019
Earlier work this paper cites.
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., Toutanova, K., Jones, L., Kelcey, M., Chang, M.W., Dai, A.M., Uszkoreit, J., Le, Q., Petrov, S.: Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics 7
2019
Earlier work this paper cites.
Lee, K., Chang, M.W., Toutanova, K.: Latent retrieval for weakly supervised open domain question answering. In: Proceedings of ACL 2019. pp. 6086–6096 (2019), https://aclanthology.org/P19-1612
2019
Earlier work this paper cites.
Nogueira, R., Lin, J.: From doc2query to doctttttquery (2019), https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery.pdf
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners (2019), https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
2019
Earlier work this paper cites.
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: A multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of ICLR 2019 (2019), https://openreview.net/forum?id=rJ4km2R5t7
2019
Earlier work this paper cites.
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., Yih, W.t.: Dense passage retrieval for open-domain question answering. In: Proceedings of EMNLP 2020. pp. 6769–6781 (2020), https://aclanthology.org/2020.emnlp-main.550
2020
Earlier work this paper cites.
Khattab, O., Zaharia, M.: Colbert: Efficient and effective passage search via contextualized late interaction over BERT. In: Proceedings of SIGIR 2020. pp. 39–48 (2020), https://doi.org/10.1145/3397271.3401075
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21
2020
Earlier work this paper cites.
2021
Cited alongside, same era.
Gao, L., Callan, J.: Condenser: a pre-training architecture for dense retrieval. In: Proceedings of EMNLP 2021. pp. 981–993 (2021), https://aclanthology.org/2021.emnlp-main.75
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Gao, L., Dai, Z., Callan, J.: COIL: Revisit exact lexical match in information retrieval with contextualized inverted list. In: Proceedings of NAACL 2021. pp. 3030–3042 (2021), https://aclanthology.org/2021.naacl-main.241
2021
Cited alongside, same era.
Ma, X., Guo, J., Zhang, R., Fan, Y., Cheng, X.: Pre-train a discriminative text encoder for dense retrieval via contrastive span prediction. In: Proceedings of SIGIR 2022. pp. 848–858 (2022), https://doi.org/10.1145/3477495.3531772
2022
Closest in time.
Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., Zaharia, M.: Colbertv2: Effective and efficient retrieval via lightweight late interaction. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022. pp. 3715–3734 (2022), https://doi.org/10.18653/v1/2022.naacl-main.272
2022
Closest in time.
2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hofstätter, S., Lin, S., Yang, J., Lin, J., Hanbury, A.: Efficiently teaching an effective dense retriever with balanced topic aware sampling. In: Proceedings of SIGIR 2021. pp. 113–122 (2021), https://doi.org/10.1145/3404835.3462891
2021
Cited alongside, same era.
Lu, S., He, D., Xiong, C., Ke, G., Malik, W., Dou, Z., Bennett, P., Liu, T.Y., Overwijk, A.: Less is more: Pretrain a strong Siamese encoder for dense text retrieval using a weak decoder. In: Proceedings of EMNLP 2021. pp. 2780–2791 (2021), https://aclanthology.org/2021.emnlp-main.220
2021
Cited alongside, same era.
Qu, Y., Ding, Y., Liu, J., Liu, K., Ren, R., Zhao, W.X., Dong, D., Wu, H., Wang, H.: RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering. In: Proceedings of NAACL 2021. pp. 5835–5847 (2021), https://aclanthology.org/2021.naacl-main.466
2021
Cited alongside, same era.
Ren, R., Lv, S., Qu, Y., Liu, J., Zhao, W.X., She, Q., Wu, H., Wang, H., Wen, J.R.: PAIR: Leveraging passage-centric similarity relation for improving dense passage retrieval. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. pp. 2173–2183 (2021), https://aclanthology.org/2021.findings-acl.191
2021
Cited alongside, same era.
Ren, R., Qu, Y., Liu, J., Zhao, W.X., She, Q., Wu, H., Wang, H., Wen, J.: Rocketqav2: A joint training method for dense passage retrieval and passage re-ranking. In: Proceedings of EMNLP 2021. pp. 2825–2835 (2021), https://doi.org/10.18653/v1/2021.emnlp-main.224
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Xiong, L., Xiong, C., Li, Y., Tang, K., Liu, J., Bennett, P.N., Ahmed, J., Overwijk, A.: Approximate nearest neighbor negative contrastive learning for dense text retrieval. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 (2021), https://openreview.net/forum?id=zeFrfgyZln
2021
Cited alongside, same era.
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., Ma, S.: Optimizing dense retrieval model training with hard negatives. In: SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021. pp. 1503–1512 (2021), https://doi.org/10.1145/3404835.3462880
2021
Cited alongside, same era.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Zhang, H., Gong, Y., Shen, Y., Lv, J., Duan, N., Chen, W.: Adversarial retriever-ranker for dense text retrieval. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 (2022), https://openreview.net/forum?id=MR7XubKUFB
2022
Closest in time.
2022
Closest in time.
Zhou, K., Gong, Y., Liu, X., Zhao, W.X., Shen, Y., Dong, A., Lu, J., Majumder, R., Wen, J.R., Duan, N., et al.: Simans: Simple ambiguous negatives sampling for dense text retrieval. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2022)
2022
Closest in time.
Zhou, K., Zhang, B., Zhao, W.X., Wen, J.R.: Debiased contrastive learning of unsupervised sentence representations. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 6120–6130 (2022)
2022
Closest in time.
Lin, Z., Gong, Y., Liu, X., Zhang, H., Lin, C., Dong, A., Jiao, J., Lu, J., Jiang, D., Majumder, R., et al.: Prod: Progressive distillation for dense retrieval. In: Proceedings of the ACM Web Conference 2023. pp. 3299–3308 (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Zhou, Y.J., Yao, J., Dou, Z.C., Wu, L., Wen, J.R.: Dynamicretriever: A pre-trained model-based ir system without an explicit index. Machine Intelligence Research pp. 1–13 (2023)
2023
Closest in time.