Filling Knowledge Gaps in Text for Machine Reading

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Long Short-Term Memory-Networks for Machine Reading (2016, background): “…et al., 2011), knowledge base population and construction (Ji and Grishman, 2011; Niu et al., 2012; Carlson et al., 2010), ontology learning (Poon and Domingos, 2010b), textual entailment (Dagan et al., 2005), and the creation of proposition stores (Pe˜nas and Hovy, 2010; Schubert and Tong, 2003).”
An Entailment-Based Approach to the QA4MRE Challenge (2012, background): “Recent work on learning event narratives ("scripts"), e.g., (Chambers and Jurafsky, 2008) and building proposition stores (Van Durme et al., 2009; Penas and Hovy, 2010) offers some exciting new possibilities in this direction.”
Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information (2018, background): “Penas and Hovy (2010) identify the notion of missing information similar to us, but they ﬁll the knowledge gaps in a text with the help of external knowledge bases, whereas we instead ask clariﬁcation questions.”