Autor:	Mahapatra, Suchismit, Blagojevic, Vladimir, Bertorello, Pablo, Kumar, Prasanna
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Long-form question answering (LFQA) tasks require retrieving the documents pertinent to a query, using them to form a paragraph-length answer. Despite considerable progress in LFQA modeling, fundamental issues impede its progress: i) train/validation/test dataset overlap, ii) absence of automatic metrics and iii) generated answers not being "grounded" in retrieved documents. This work addresses every one these critical bottlenecks, contributing natural language inference/generation (NLI/NLG) methods and metrics that make significant strides to their alleviation. Comment: 8 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.13432 Zobrazit plný text záznamu View this record from Arxiv