The Winograd Schema Challenge and Reasoning about Correlation

By Daniel Bailey, A. Harrison, Yuliya Lierler, Vladimir Lifschitz, Julian Michael. 2015

55 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Jianfeng Gao
2. Xiaodong Liu
3. Yejin Choi
4. Adam Trischler
5. Shuohang Wang
6. Richard Socher
7. Caiming Xiong
8. Weizhu Chen

In Evaluation & Benchmarks

1. Jing Jiang
2. Yelong Shen
3. Jianfeng Gao
4. Moin Nabi
5. Hannes Schulz
6. Ali Emami
7. Xiaodong Liu
8. Quoc V. Le

Citations in the corpus

• A Simple Method for Commonsense Reasoning (2018, methodology): “ally commonsense information. 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]. In particular, Rahman and Ng [25] employ human annotators to build more supervised tra”
• The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution (2018, background): “Previous approaches to common sense reasoning, based on logical formalisms (Bailey et al., 2015) or deep neural models (Liu et al.”
• Contrastive Self-Supervised Learning for Commonsense Reasoning (2020, background): “knowledge bases (KB), rule-based reasoning, or hand-crafted features (Bailey et al., 2015; Schüller, 2014; Sharma et al., 2015).”
• A Knowledge Hunting Framework for Common Sense Reasoning (2018, background): “Previous approaches to common sense reasoning, for instance based on logical formalisms ( Bailey et al., 2015) or deep neural models ( Liu et al., 2016), have solved only restricted subsets of the WSC with high precision.”
• Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (2019, methodology): “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): “There have been efforts to encode such knowledge directly, using logical formalisms (Bailey et al., 2015) or by using deep learning models (Liu et al., 2016a); however, these approaches have so far solved only restricted subsets of WSC questions with high precision, and show limited ability to…”