Generalised Winograd Schema and its Contextuality
Kin Ian Lo Mehrnoosh Sadrzadeh Shane Mansfield
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.