Colorless green ideas do sleep furiously

Gradient acceptability and the nature of the grammar

Datos Bibliográficos

ID20344262
AutoresJon Sprouse (0000-0002-8875-3204, University of Connecticut, autor de correspondencia), Beracah Yankama (Massachusetts Institute of Technology), Sagar Indurkhya (Massachusetts Institute of Technology), Sandiway Fong (University of Arizona), Robert C Berwick (0000-0002-1061-1871, Massachusetts Institute of Technology)
Año2018
Volumen35
Número3
Páginas575-599
Fecha de publicación2018-09-25
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaThe Linguistic Review (JOURNAL)
Identificadores de la revistaISSN: 0167-6318 • E-ISSN: 1613-3676
EditorialWalter de Gruyter GmbH (PUBLISHER • DE)
DOI10.1515/tlr-2018-0005
OpenAlexW2805227907
IdiomaEN
Citas recibidas15
Referencias citadas11

In their recent paper, Lau, Clark, and Lappin explore the idea that the probability of the occurrence of word strings can form the basis of an adequate theory of grammar (Lau, Jey H., Alexander Clark & 15 Shalom Lappin. 2017. Grammaticality, acceptability, and probability: A prob- abilistic view of linguistic knowledge. Cognitive Science 41(5):1201–1241). To make their case, they present the results of correlating the output of several probabilistic models trained solely on naturally occurring sentences with the gradient acceptability judgments that humans report for ungrammatical sentences derived from roundtrip machine translation errors. In this paper, we first explore the logic of the Lau et al. argument, both in terms of the choice of evaluation metric (gradient acceptability), and in the choice of test data set (machine translation errors on random sentences from a corpus). We then present our own series of studies intended to allow for a better comparison between LCL’s models and existing grammatical theories. We evaluate two of LCL’s probabilistic models (trigrams and recurrent neural network) against three data sets (taken from journal articles, a textbook, and Chomsky’s famous colorless-green-ideas sentence), using three evaluation metrics (LCL’s gradience metric, a categorical version of the metric, and the experimental-logic metric used in the syntax literature). Our results suggest there are very real, measurable cost-benefit tradeoffs inherent in LCL’s models across the three evaluation metrics. The gain in explanation of gradience (between 13% and 31% of gradience) is offset by losses in the other two metrics: a 43%-49% loss in coverage based on a categorical metric of explaining acceptability, and a loss of 12%-35% in explaining experimentally-defined phenomena. This suggests that anyone wishing to pursue LCL’s models as competitors with existing syntactic theories must either be satisfied with this tradeoff, or modify the models to capture the phenomena that are not currently captured

Grammar · Grammaticality · Linguistics · Natural language processing · Probabilistic logic · Sentence · Computer Science · Natural Language Processing Techniques · Neurobiology of Language and Bilingualism · Philosophy · Topic Modeling · Artificial Intelligence