Fetching the paper…
Reading the bibliography…
This paper presents the participation of Macquarie University and the Australian National University for Task B Phase B of the 2020 BioASQ Challenge (BioASQ8b).
Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning 8
1992
Earlier work this paper cites.
Dang, H.T.: Duc 2005: Evaluation of question-focused summarization systems. In: Proceedings of the Workshop on Task-Focused Summarization and Question Answering. pp. 48–55 (2006)
2006
Earlier work this paper cites.
2017
Earlier work this paper cites.
Hill, A., Raffin, A., Ernestus, M., Gleave, A., Kanervisto, A., Traore, R., Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., Wu, Y.: Stable baselines. https://github.com/hill-a/stable-baselines
2018
Earlier work this paper cites.
Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 328–339. Melbourne, Australia (Jul 2018). https://doi.org/10.18653/v1/P18-1031, https://www.aclweb.org/anthology/P18-1031
2018
Earlier work this paper cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding pp. 4171–4186 (jun 2019), https://www.aclweb.org/anthology/N19-1423/
2019
Cited alongside, same era.
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36
2019
Cited alongside, same era.
Liu, Y., Lapata, M.: Text summarization with pretrained encoders. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 3730–3740. Hong Kong, China (Nov 2019). https://doi.org/10.18653/v1/D19-1387, https://www.aclweb.org/anthology/D19-1387
2019
Cited alongside, same era.
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 7871–7880. Online (Jul 2020), https://www.aclweb.org/anthology/2020.acl-main.703
2020
Closest in time.
Mekala, D., Shang, J.: Contextualized weak supervision for text classification. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 323–333. Online (Jul 2020), https://www.aclweb.org/anthology/2020.acl-main.30
2020
Closest in time.
Mollá, D., Jones, C.: Classification betters regression in query-based multi-document summarisation techniques for question answering. In: Cellier, P., Driessens, K. (eds.) Machine Learning and Knowledge Discovery in Databases. pp. 624–635. Springer International Publishing, Cham (2020)
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Jones, C.R.: Reinforcement Learning For Query-based Multi-document Extractive Summarisation. Master’s thesis, Macquarie University (Jan 2020)
2020
Cited alongside, same era.
2020
Closest in time.