1. Akkalyoncu Yilmaz, Z., Yang, W., Zhang, H., & Lin, J. (2019). Cross-domain modeling of sentence-level evidence for document retrieval. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCN), ACL, Hong Kong, China, (pp. 3488–3494).
2. Boualili, L., Moreno, J. G., & Boughanem, M. (2020). MarkedBERT: Integrating traditional IR cues in pre-trained language models for passage retrieval (pp. 1977–1980). New York, NY, USA: Association for Computing Machinery.
3. Câmara, A., & Hauff, C. (2020). Diagnosing bert with retrieval heuristics. In J. M. Jose, E. Yilmaz, J. Magalhães, P. Castells, N. Ferro, M. J. Silva, & F. Martins (Eds.), Advances in information retrieval (pp. 605–618). Cham: Springer International Publishing.
4. Chen, X., Li, C., He, B., & Sun, Y. (2019). UCAS at TREC-2019 deep learning track. In Voorhees EM, Ellis A (eds) Proceedings of the Twenty-Eighth Text REtrieval Conference, TREC 2019, (Vol. 1250). 2019, National Institute of Standards and Technology (NIST), NIST Special Publication: Gaithersburg, Maryland, USA.
5. Chen, X., He, B., Sun, L., & Sun, Y. (2020). ICIP at TREC-2020 deep learning track. In Voorhees EM, Ellis A (eds) Proceedings of the Twenty-Ninth Text REtrieval Conference, TREC 2020, (Vol. 1266) . National Institute of Standards and Technology (NIST), NIST Special Publication: Gaithersburg, Maryland, USA.