in Italian enabled by the recent availability of a large scale annotated corpus

In italian enabled by the recent availability of a

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in Italian, enabled by the recent availability of a large-scale annotated corpus, SQuAD-IT. The ex- perimental results confirm the robustness of the adopted Transformer-based architecture, with a significant improvement with respect to earlier neural architectures. This result paves the way to the development of portable, robust and accurate neural models for QA in Italian, and future work will certainly consider other possible extensions of the adopted model. References Petr Baudiˇs and Jan ˇ Sediv´y. 2015. Modeling of the Question Answering Task in the YodaQA Sys- tem. In Josanne Mothe, Jacques Savoy, Jaap Kamps, Karen Pinel-Sauvagnat, Gareth Jones, Eric San Juan, Linda Capellato, and Nicola Ferro, edi- tors, Experimental IR Meets Multilinguality, Mul- timodality, and Interaction , pages 222–228, Cham. Springer International Publishing. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on freebase from question-answer pairs. In EMNLP , pages 1533– 1544. ACL. E. Brill, S. Dumais, M. Banko, Eric Brill, Michele Banko, and Susan Dumais. 2002. An Analysis of the AskMSR Question-Answering System. In Pro- ceedings of EMNLP 2002 , January. Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading wikipedia to answer open- domain questions. In Proceedings of the 55th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 1870– 1879. Christopher Clark and Matt Gardner. 2018. Simple and effective multi-paragraph reading comprehen- sion. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers) , pages 845–855, Melbourne, Australia, July. Association for Computational Lin- guistics. Danilo Croce, Alexandra Zelenanska, and Roberto Basili. 2018. Neural learning for question answer- ing in italian. In Chiara Ghidini, Bernardo Magnini, Andrea Passerini, and Paolo Traverso, editors, AI*IA 2018 – Advances in Artificial Intelligence , pages 389–402, Cham. Springer International Publishing. Danilo Croce, Alexandra Zelenanska, and Roberto Basili. 2019. Enabling deep learning for large scale question answering in italian. Intelligenza Artifi- ciale , 13(1):49–61. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Short Papers) , pages 4171–4186, Minneapolis, Minnesota, June. Associ- ation for Computational Linguistics. David A. Ferrucci, Eric W. Brown, Jennifer Chu- Carroll, James Fan, David Gondek, Aditya Kalyan- pur, Adam Lally, J. William Murdock, Eric Nyberg, John M. Prager, Nico Schlaefer, and Christopher A. Welty. 2010. Building Watson: An Overview of the DeepQA Project. AI Magazine , 31(3):59–79. Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Ji- tendra Malik. 2013. Rich feature hierarchies for ac- curate object detection and semantic segmentation.
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