# Generalised Winograd Schema and its Contextuality

arXiv: https://arxiv.org/abs/2308.16498 · HTML: https://mlsanity.com/paper/2308.16498/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge](https://mlsanity.com/paper/2005.05763/) (2020)
- [The Defeat of the Winograd Schema Challenge](https://mlsanity.com/paper/2201.02387/) (2022)
- [A Knowledge Hunting Framework for Common Sense Reasoning](https://mlsanity.com/paper/1810.01375/) (2018)
- [On the Quantum-like Contextuality of Ambiguous Phrases](https://mlsanity.com/paper/2107.14589/) (2021)
- [On Contextuality in Behavioral Data](https://mlsanity.com/paper/1508.04751/) (2015)
- [A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT](https://mlsanity.com/paper/2208.05720/) (2022)
- [The Causal Structure of Semantic Ambiguities](https://mlsanity.com/paper/2206.06807/) (2022)

## Built on by (most cited first, 4 of 4)

- [The Future of Large Language Model Pre-training is Federated](https://mlsanity.com/paper/2405.10853/) (2024)
- [Quantum-Like Contextuality in Large Language Models](https://mlsanity.com/paper/2412.16806/) (2024)
- [Developments in Sheaf-Theoretic Models of Natural Language Ambiguities](https://mlsanity.com/paper/2402.04505/) (2024)
- [Photon: Federated LLM Pre-Training](https://mlsanity.com/paper/2411.02908/) (2024)

## Abstract (arXiv)

Ambiguities in natural language give rise to probability distributions over interpretations. The distributions are often over multiple ambiguous words at a time; a multiplicity which makes them a suitable topic for sheaf-theoretic models of quantum contextuality. Previous research showed that different quantitative measures of contextuality correlate well with Psycholinguistic research on lexical ambiguities. In this work, we focus on coreference ambiguities and investigate the Winograd Schema Challenge (WSC), a test proposed by Levesque in 2011 to evaluate the intelligence of machines. The WSC consists of a collection of multiple-choice questions that require disambiguating pronouns in sentences structured according to the Winograd schema, in a way that makes it difficult for machines to determine the correct referents but remains intuitive for human comprehension. In this study, we propose an approach that analogously models the Winograd schema as an experiment in quantum physics. However, we argue that the original Winograd Schema is inherently too simplistic to facilitate contextuality. We introduce a novel mechanism for generalising the schema, rendering it analogous to a Bell-CHSH measurement scenario. We report an instance of this generalised schema, complemented by the human judgements we gathered via a crowdsourcing platform. The resulting model violates the Bell-CHSH inequality by 0.192, thus exhibiting contextuality in a coreference resolution setting.