A Review of Winograd Schema Challenge Datasets and Approaches
arXiv:2004.13831
Abstract
The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.
References in corpus (7)
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- A Simple Method for Commonsense Reasoning
- The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
- A Surprisingly Robust Trick for Winograd Schema Challenge
- Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
- Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
- A Knowledge Hunting Framework for Common Sense Reasoning
Cited by in corpus (7)
- Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge
- Attention-based Contrastive Learning for Winograd Schemas
- Künstliche Intelligenz, quo vadis?
- Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction
- The Case for Claim Difficulty Assessment in Automatic Fact Checking
- Precise Task Formalization Matters in Winograd Schema Evaluations
- Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models