Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs

By Peter Schüller. 2014

48 citations recorded by the source corpus.

Subfields: Natural Language Processing, Prompting & Reasoning

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Citations in the corpus

A Simple Method for Commonsense Reasoning (2018, background): “Previous attempts on solving the Winograd Schema Challenge usually involve heavy utilization of annotated knowledge bases, rule-based reasoning, or hand-crafted features [22, 23, 24].”
A Hybrid Neural Network Model for Commonsense Reasoning (2019, methodology): “While traditional methods of commonsense reasoning rely heavily on human-crafted features and knowledge bases (Rahman and Ng, 2012a; Sharma et al., 2015; Sch¨uller, 2014; Bailey et al., 2015; Liu et al., 2017), we explore in this study machine learning approaches using deep neural networks (DNN).”
Contrastive Self-Supervised Learning for Commonsense Reasoning (2020, background): “Traditional attempts on commonsense reasoning usually involve heavy utilization of annotated knowledge bases (KB), rule-based reasoning, or hand-crafted features (Bailey et al., 2015; Sch¨uller, 2014; Sharma et al., 2015).”
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (2019, background): “Sharma et al. (2015); Schüller (2014); Bailey et al. (2015); Liu et al. (2017) utilized expensive knowledge bases in their reasoning processes.”
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge (2018, methodology): “The systems directed towars the Original Wino-grad questions include Sch¨uller (2014), who use principles from relevance theory to show correct disambiguation of 4 of the Winograd instances; Sharma et al. (2015)’s knowledge-hunting module aimed at a subset of 71 instances that exhibit causal…”