LINGUIST List 11.2024

Sat Sep 23 2000

Review: Tesar & Smolensky: Learnability in OT

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  1. Tania Avgustinova, Book review: Tesar and Smolensky (2000): Learnability in OT

Message 1: Book review: Tesar and Smolensky (2000): Learnability in OT

Date: Wed, 20 Sep 2000 13:28:53 +0200
From: Tania Avgustinova <taniaCoLi.Uni-SB.DE>
Subject: Book review: Tesar and Smolensky (2000): Learnability in OT

Bruce Tesar and Paul Smolensky (2000)
Learnability in Optimality Theory
The MIT Press, Cambridge, Massachusetts
140 pages

Reviewed by Tania Avgustinova, Saarland University


SYNOPSIS

The book is concerned with the application of formal 
learning theory to the problem of language acquisition. The 
OT implication for language learnability is examined. The 
main claim is that the very core principles of OT lead to 
learning principles of constraint demotion which, in turn, 
are the basis for a family of algorithms for inferring 
constraint rankings from linguistic forms. The learning 
procedure proposed here by Tesar and Smolensky (T&S, 
henceforth) learns both the correct interpretations and the 
correct grammar simultaneously. 

The book is organised as follows. 

Chapter 1 (pages 1-18) is devoted to laying out the larger 
context of this work, and addresses issues of learnability 
and Universal Grammar (UG), as well as the problem of 
learning hidden structure. The background, as presented, 
naturally leads to the central claim of the book, i.e. "that 
OT provides sufficient structure at the level of grammatical 
framework itself to allow general but grammatically informed 
learning algorithms to be formally defined". T&S's approach 
employs a decomposition of learning into two central sub-
problems: (i) assigning a structural description to an overt 
linguistic form given a grammar that may not be correct 
(RIP: Robust Interpretative Parsing), and (ii) learning of a 
constraint ranking from a set of full structural 
descriptions (CD: Constraint Demotion). 

Chapter 2 (pages 19-32) offers an overview of OT, including 
illustrations with OT analyses of syllable structure and 
clausal subject distribution. 

Chapter 3 (pages 33-52) discusses the CD principle stating 
that constraints violated by grammatical structural 
descriptions must be demoted in the ranking below 
constraints violated by competing structural descriptions. 

Chapter 4 (pages 53-74) presents experimental results in 
overcoming ambiguity in overt forms, using a computer 
implementation of RIP/CD which is applied to an OT system 
for metrical stress. This is an illustration of how the 
strategy of iterating between structure assignment and 
ranking adjustment actually works. 

Chapter 5 (pages 75-84) addresses key issues in language 
learning, e.g., the subset principle, richness of the base 
and acquisition theory. T&S consider the prospects for 
extending the same iterative strategy (embodied by RIP/CD) 
with respect to the language-specific inventory, in order to 
include the simultaneous learning of rankings and lexical 
underlying forms.

Chapter 6 (pages 85-90) revisits the relationship between 
learnability and linguistic theory (or UG).

Chapter 7 (pages 91-110) contains formalisation and proofs 
of the correctness and data complexity of CD. 

Chapter 8 (pages 111-128) contains algorithms for performing 
production-directed parsing.

Finally, there are notes (pages 129-132), a list of 
references (pages 133-138) and an index (pages 139-140).


COMMENTS

As the learning proposal presented and evaluated in this 
book is tightly bound to the central principles of OT, its 
success can be taken as evidence in favour of T&S's major 
claim that OT makes possible a particularly strong union of 
the interests of language learnability and linguistic 
theory. 

In OT, interaction of constraints is not only possible but 
explanatory crucial. Cross-linguistic variation is explained 
by variation in the relative ranking of the same 
constraints, and hence, is only possible to the extent that 
constraints interact. The CD learning algorithm not only 
tolerates constraint interaction, but is based entirely on 
it. Operates on loser/ winner pairs, CD deduces consequences 
for the grammar from the fact that the winner (a positive 
example provided to the grammar learner) must be more 
harmonic than the loser (an alternative sub-optimal parse on 
the same input presumably generated by the grammar learner). 
Whether the winner/loser pair is informative depends both on 
the winner and on the loser. 

Importantly, constraint re-ranking is defined entirely in 
terms of demotion, i.e. all "movement" of constraints is 
downward in the hierarchy. This allows to avoid disjunctions 
which are notoriously problematic in general computational 
theory. Using demotion only - rather than promotion - 
results in moving the constraints corresponding to the 
winner's violation marks (which are contained in a 
conjunction), while a hypothetical promotion would move the 
constraints corresponding to the loser's marks (which are 
contained in a disjunction). In the case of promotion, it is 
not clear which of the loser's violations should be 
promoted: all, some or just one of them. With demotion, 
there is no such choice to be made, since all constraints 
violated by the winner must be dominated by the highest 
ranked loser mark. The impressing result is that because CD 
only demotes constraints as far as necessary, a constraint 
never gets demoted below its target position, and will not 
be demoted further once reaching it.

Starting with all constraints in Con ranked in a top 
stratum, and applying CD to informative positive evidence as 
long as such exists, the process converges on a stratified 
hierarchy, such that all totally ranked refinements of that 
hierarchy correctly account for the learning data. Note that 
while the target (e.g., adult) grammars are taken to be 
totally ranked hierarchies, CD operates within a hypothesis 
space constituted by stratified hierarchies, which space is 
widely uncommitted on the relative ranking of constraints. 

The components of T&S's learning system are all strongly 
shaped by the optimisation character of the grammar being 
acquired. The particular structure of grammar under OT - 
optimisation relative to a hierarchy of constraints - 
enables them to tie learning the lexicon of underlying forms 
to the basic operation of the grammar - pairing output 
structures to inputs - as well as to the assignment of 
hidden structure to overt learning data. 

Defining grammaticality in terms of optimisation over 
violable constraints, so that constraint interaction can be 
made the main explanatory mechanism, is an attractive 
feature of OT in general. The results of T&S provide 
convincing evidence that OT, linguistic explanation and 
learnability work together. The authors give a positive 
answer to the question whether there are reliable, efficient 
means for finding a ranking of a given set of constraints 
that correctly yields a given set of grammatical structural 
descriptions. On the other hand, the question if it is 
necessary that informative sub-optimal forms or full 
structural descriptions of positive examples be provided to 
the learner is answered negatively. 

T&S's work is an excellent and rigorous presentation of OT 
in action. It contains an interesting proposal for how a 
learner, provided with the universal elements of any OT UG 
system, and the overt parts of forms grammatical with 
respect to some grammar admitted by the UG, could learn the 
grammar, the structural descriptions and the lexicon. 
 
This book can be strongly recommended for introductory and 
advanced courses in both theoretical and applied 
linguistics.

===================================
Dr. Tania Avgustinova
Computational Linguistics, Saarland University 
Postfach 151150, 66041 Saarbruecken, Germany
taniacoli.uni-sb.de, http://www.coli.uni-sb.de/~tania/
(+49) (681) 302.4504 (phone) 
(+49) (681) 302 4115 (secretary)
(+49) (681) 302.4700 (fax) 

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