There has also been significant empirical evidence supporting the argument that humans' semantic inferences are uncertain and context-sensitive (Poesio and Artstein, 2005;Versley, 2008;Simons et al., 2010;Recasens et al., 2011;de Marneffe et al., 2012;Passonneau et al., 2012;Pavlick and Callison-Burch, 2016a,b;, among others) suggesting computational models would benefit from focusing on ''speaker meaning'' over ''sentence meaning'' when it comes to NLI Westera and Boleda, 2019).
in this paper, we assume that NLP will maintain this hands-off approach to NLI, avoiding definitions of what inferences humans should make or which types of knowledge they should invoke.