Interpreting Winograd Schemas Via the SP Theory of Intelligence and Its Realisation in the SP Computer Model
This work addresses a specific challenge in natural language understanding for AI researchers, but it is incremental as it builds on existing theories without achieving full unsupervised learning.
The paper tackles the problem of interpreting Winograd Schema sentences, which are challenging for AI systems, by proposing the SP System's approach using discontinuous associations of linguistic features to resolve pronoun references, but notes that the system currently requires pre-supplied knowledge and has not yet demonstrated learning this knowledge autonomously.
In 'Winograd Schema' (WS) sentences like "The city councilmen refused the demonstrators a permit because they feared violence" and "The city councilmen refused the demonstrators a permit because they advocated revolution", it is easy for adults to understand what "they" refers to but can be difficult for AI systems. This paper describes how the SP System -- outlined in an appendix -- may solve this kind of problem of interpretation. The central idea is that a knowledge of discontinuous associations amongst linguistic features, and an ability to recognise such patterns of associations, provides a robust means of determining what a pronoun like "they" refers to. For any AI system to solve this kind of problem, it needs appropriate knowledge of relevant syntax and semantics which, ideally, it should learn for itself. Although the SP System has some strengths in unsupervised learning, its capabilities in this area are not yet good enough to learn the kind of knowledge needed to interpret WS examples, so it must be supplied with such knowledge at the outset. However, its existing strengths in unsupervised learning suggest that it has potential to learn the kind of knowledge needed for the interpretation of WS examples. In particular, it has potential to learn the kind of discontinuous association of linguistic features mentioned earlier.