11.2024, Review: Tesar & Smolensky: Learnability in OT

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LINGUIST List:  Vol-11-2024. Sat Sep 23 2000. ISSN: 1068-4875.

Subject: 11.2024, Review: Tesar & Smolensky: Learnability in OT

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1)
Date:  Wed, 20 Sep 2000 13:28:53 +0200
From:  Tania Avgustinova <tania at CoLi.Uni-SB.DE>
Subject:  Book review: Tesar and Smolensky (2000): Learnability in OT

-------------------------------- Message 1 -------------------------------

Date:  Wed, 20 Sep 2000 13:28:53 +0200
From:  Tania Avgustinova <tania at CoLi.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
tania at coli.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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