[3] Giuseppe Attardi. Wikiextractor. https://github.com/attardi/wikiextractor, 2015.
[4] Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. "O'Reilly Media, Inc.", 2009.
[5] Iacer Calixto, Alessandro Raganato, and Tommaso Pasini. Wikipedia entities as rendezvous across languages: Grounding multilingual language models by predicting Wikipedia hyperlinks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2021.
[6] Rakesh Chada and Pradeep Natarajan. FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6081-6090, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics.
[7] Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, and Sanjiv Kumar. Pre-training tasks for embedding-based large-scale retrieval. In International Conference on Learning Representations, 2020.
[8] Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, and Ningyu Zhang. LightNER: A lightweight tuning paradigm for low-resource NER via pluggable prompting. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2374-2387, Gyeongju, Republic of Korea, October 2022. International Committee on Computational Linguistics.
[9] Jason P.C. Chiu and Eric Nichols. Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 4:357-370, 2016.
[10] Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. Template-based named entity recognition using BART. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, August 2021.
[11] Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. Results of the WNUT2017 shared task on novel and emerging entity recognition. In Proceedings of the 3rd Workshop on Noisy User-generated Text, September 2017.
[12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), June 2019.
[13] Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2368-2378, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.
[14] Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Güney, Volkan Cirik, and Kyunghyun Cho. Searchqa: A new q&a dataset augmented with context from a search engine. ArXiv, abs/1704.05179, 2017.
[15] Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. MRQA 2019 shared task: Evaluating generalization in reading comprehension. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, November 2019.
[16] Tianyu Gao, Adam Fisch, and Danqi Chen. Making pre-trained language models better few-shot learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3816-3830, Online, August 2021. Association for Computational Linguistics.
[17] Tianyu Gao, Xingcheng Yao, and Danqi Chen. SimCSE: Simple contrastive learning of sentence embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, November 2021.
[18] Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, and Avi Sil. Span selection pre-training for question answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2773-2782, Online, July 2020. Association for Computational Linguistics.
[19] Karl Moritz Hermann, Tomás Kočisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. Teaching machines to read and comprehend. In Proceedings of the 28th International Conference on Neural Information Processing Systems-Volume 1, pages 1693–1701, 2015.