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Título:	The LAMBADA dataset: word prediction requiring a broad discourse context
Autor/a:	Boleda, Gemma; Paperno, Denis; Kruszewski, German; Lazaridou, Angeliki; Pham, Quan Ngoc; Bernardi, Raffaella; Pezzelle, Sandro; Baroni, Marco; Fernandez, Raquel
Abstract:	We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-ofthe-art language models reaches accuracy above 1% on this novel benchmark. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text.
Abstract:	This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655577 (LOVe); ERC 2011 Starting Independent Research Grant n. 283554 (COMPOSES); NWO VIDI grant n. 276-89-008 (Asymmetry in Conversation).
Materia(s):	-LAnguage Modeling Broadened to Account for Discourse Aspects -Natural Language Processing -Language modeling -Discourse -Deep learning
Derechos:	© ACL, Creative Commons Attribution 4.0 License http://creativecommons.org/licenses/by/4.0/
Tipo de documento:	Objeto de conferencia Artículo - Versión publicada
Editor:	ACL (Association for Computational Linguistics)
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