Towards Addressing the Winograd Schema Challenge - Building and Using a Semantic Parser and a Knowledge Hunting Module

By Arpit Sharma, N. Vo, N. Vo, Somak Aditya, Chitta Baral. 2015

86 citations recorded by the source corpus.

Subfields: Natural Language Processing, Knowledge Graphs & Representation

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Citations in the corpus

WinoGrande (2019, background): “nippets. This IR-oriented approach comes from an important line of work in coreference resolution (Kobdani et al.,2011; Ratinov and Roth,2012;Bansal and Klein,2012; Zheng et al.,2013;Peng et al.,2015;Sharma et al., 2015). Ensemble Neural LMs Trinh and Le(2018) is one of the ﬁrst attempts to apply a ”
A Simple Method for Commonsense Reasoning (2018, methodology): “As previous systems collect relevant data from knowledge bases after observing questions during evaluation [25, 26], we also explore using this option.”
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).”
Answering Complex Questions Using Open Information Extraction (2017, background): “On-the-ﬂy tuples from text: To handle questions from new domains not covered by the training set, we extract additional tuples on the ﬂy from S (similar to Sharma et al. (2015)).”
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering (2018, methodology): “Other semantic parsers such as K-parser (Sharma et al. 2015), represent sentences using meaningful relations.”
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 Knowledge Hunting Framework for Common Sense Reasoning (2018, methodology): “Other systems (Kobdani et al., 2011; Ratinov and Roth, 2012; Bansal and Klein, 2012; Zheng et al., 2013; Peng et al., 2015; Sharma et al., 2015) rely on similar techniques, i.e., using search-query counts or co-occurrence statistics and word alignment methods to relate antecedents with pronouns.”
An Adversarial Winograd Schema Challenge at Scale (2019, background): “This IR-oriented approach comes from an important line of work in coreference resolution (Kobdani et al., 2011; Ratinov and Roth, 2012; Bansal and Klein, 2012; Zheng et al., 2013; Peng et al., 2015; Sharma et al., 2015).”
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).”
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge (2018, methodology): “Previous work on end-to-end knowledge-hunting mechanisms for the WSC includes a recent framework that compares query counts of evidence retrieved online for the competing antecedents (Sharma et al., 2015).”
Addressing the Winograd Schema Challenge as a Sequence Ranking Task (2018, background): “Sharma et al. (2015) build a semantic parser and Sch¨uller (2014) use syntactic dependency annotation and knowledge base linking in order to solve WSC problems.”