PhilSci Archive

On the Winograd Schema: Situating Language Understanding in the Data-Information-Knowledge Continuum

Saba, Walid (2019) On the Winograd Schema: Situating Language Understanding in the Data-Information-Knowledge Continuum. [Preprint]

[img]
Preview
Text
SELIEA-F-SabaW.152.pdf

Download (1MB) | Preview

Abstract

The Winograd Schema (WS) challenge, proposed as an alternative to the Turing Test, has become the new standard for evaluating progress in natural language understanding (NLU). In this paper we will not however be concerned with how this challenge might be addressed. Instead, our aim here is threefold: (i) we will first formally „situate‟ the WS challenge in the data-information-knowledge continuum, suggesting where in that continuum a good WS resides; (ii) we will show that a WS is just a special case of a more general phenomenon in language understanding, namely the missing text phenomenon (henceforth, MTP) - in particular, we will argue that what we usually call thinking in the process of language understanding involves discovering a significant amount of „missing text‟ - text that is not explicitly stated, but is often implicitly assumed as shared background knowledge; and (iii) we conclude with a brief discussion on why MTP is inconsistent with the data-driven and machine learning approach to language understanding.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Preprint
Creators:
CreatorsEmailORCID
Saba, Walidwalid.saba@gmail.com
Keywords: Turing Test, Winograd Schema, Missing Text Phenomenon, Commonsense Knowledge, Semantics, Data-Driven NLU
Subjects: Specific Sciences > Mathematics > Logic
Specific Sciences > Mathematics > Ontology
Specific Sciences > Artificial Intelligence
Depositing User: Dr. WALID SABA
Date Deposited: 25 Mar 2019 16:30
Last Modified: 25 Mar 2019 16:30
Item ID: 15829
Subjects: Specific Sciences > Mathematics > Logic
Specific Sciences > Mathematics > Ontology
Specific Sciences > Artificial Intelligence
Date: May 2019
URI: https://philsci-archive.pitt.edu/id/eprint/15829

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item