Phonological constraint induction in a connnectionist network: Learning OCP-Place constraints from data
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1 PDF of article: Academia Sinica, October 30, 2013 Phonological constraint induction in a connnectionist network: Learning OCP-Place constraints from data John Alderete, Paul Tupper Simon Fraser University Stefan A. Frisch University of South Florida
2 Universal constraints One of the primary goals of linguistic theory is to explain language particular facts with universal principles. Optimality Theory: constraints are universal and innate (not learned); learning involves ranking apriori constraints Harmonic and MaxEnt grammar: constraints also given in advance, but constraint weights learned 2
3 Problems Language particular crazy rules (Blevins, Hayes): to retain predictiveness of UG constraints, need mechanism for inducing constraints Language particular constraints in language acquisition (Goad, Fikkert & Levelt, Levelt & van Oostendorp): differences between child and adult phonological processes require constraint induction Conclusion: learning the constraints themselves is an important part of learning 3
4 The argument Standard learning architecture of connectionist networks (c-nets) provides attractive advantages for constraint induction. Reasons to be optimistic about c-nets: 1. Relates linguistic analysis to psycholinguistic research 2. Connectionist systems good at capturing linguistic facts: similarity, gradience; categorical effects too 4
5 The argument, cont d 3. C-nets provide a very natural analysis of constraint induction. Connectionist constraint induction: Constraints in c-nets are soft, cf. hard constraints of symbolic computation. Constraints in c-nets are sets of connections (Smolensky). Example: Onset in BrbrNet is encoded as inhibitory links between output nodes. Learning constraints can therefore be straightforwardly modeling as constraint weight adjustment. 5
6 Outline 1. Linguistic background on Arabic 2. Formal background for connectionist networks 3. A c-net for learning the OCP in Arabic 4. Discussion and conclusion 6
7 Linguistic background Arabic morphology: roots and patterns Roots: strings of consonants Patterns: Add roots to form stems/words k-t-b write + CaaCiC ð kaatib writer Consonants in four place series (labials, coronals, dorsals, pharyngeals) Phonotactics: same series consonants tend not to co-occur in a root (classical grammarians, Greenberg, McCarthy) 7
8 Linguistic background, cont d Statistical analysis: Observed/Expected number of observed consonant pairs/ number expected by chance Observation: consonant pairs that have the same place, and are similar on other features, have very low O/E values Dubbed OCP-Place But coronal split into sonorants versus obstruents so secondary feature effects 8
9 Arabic co-occurrence by class Labial Cor stop Cor fric Dors Uvular Guttural Cor son Labial Cor stop Cor fric Dors Uvular Guttural Cor son 0.06
10 Linguistic background, cont d Some other secondary feature effects (statistically) Uvulars span dorsal and pharyngeal series Coronal stops and coronal fricatives Dorsals with uvularized coronals Pierrehumbert: Similarity predicts cooccurrence within place series 10
11 Arabic OCP by similarity O/E Observed Stoch model Similarity 11
12 Psycholinguistic findings Wordlikeness study: Jordanian Arabic speakers rate written nonsense words Lexical and linguistic factors: OCP violations (~30% variance explained) Lexical statistics (~20% variance expl.) Similarity (~20% variance expl.) Found impact of OCP-Place, differentiation of accidental and systematic gaps, and similarity effect 12
13 Connectionist Architecture Relatively structure free architecture for pattern learning (for some types of patterns, like phonotactics) Simple units with an activation level Activation level depends on weight between interconnected units Some structure assumed in our architecture (features and segments) Weights to be learned (standard technique) 13
14 Illustration: activation spread Input-output mappings: weighted sum of input activations, scaled by an activation function. 14!
15 Constraints in c-nets Subsymbolic constraints in c-nets: connections between units (Smolensky 1988) Parameters: single connection or sets of connections, negative or positive connections, constraint weight. Illustration: if connection is positive, it tries to put the receiving unit in the same state as the sending unit; constraint is can be said to be satisfied if this occurs. Brbrnet: Onset is inhibitory links between output nodes: push system to a state in which output nodes are sequences of 0-1, 0-1, which map to onset-peak 15
16 C-net for Arabic phonotactics Objective: try to build a c-net that can capture the OCP-Place effects with numerical computation Desideratum I: no apriori constraints; constraint weights set to random numbers Desideratum II: c-net should mimic human intuitions about wordlikeness Empirical challenge: can c-net learn OCPconstraints by adjusting constraint weights in response to Arabic roots? 16
17 The Autoassociator Production system (but not a realistic one): local encoding of triliteral root, tries to duplicate input in output Mature network either reproduces correct root or makes error in one or more consonants noise noise noise CLS48 17
18 The Assessor Input is feature representation of root Output interpreted as acceptability Exists = 1, Doesn t exist = -1 CLS48 18
19 Training assessor Backpropagation using output from autoassociator, from 3439 actual roots of Arabic, Correct word target = 1 Incorrect word target = -1 Learns word/ nonword through errors Hidden layer forces generalization CLS48 19
20 Results: Lexicon (a) All attested roots (c) OCP compliant roots (b) All possible roots (d) OCP violating roots
21 Results: Experiments Compare Assessor node outputs to human subject wordlikeness responses. Qualitative agreement with results for human subjects OCP ~48% variance explained Lexical statistics ~31% variance expl. Similarity ~14% variance explained Performance stable with respect to number of hidden nodes (the biggest structural variable in model) Conclusion: a relatively simple set of model parameters reproduced all of the significant effects of the psycholinguistic study 21
22 Analysis of hidden layer To what extent does the hidden layer encode the symbolic phonological generalizations? Quite a bit. CART analysis Creates a decision tree for the entire dataset based on the behavior of one hidden node at a time Statistical analysis Correlation between activation and OCP violation for each node 22
23 CART analysis example CART: method of imposing categorical analysis on messy data. Input-outputs: CART analysis looks at inputs and outputs, and finds features that are the best predictors of the data; applied recursively to produce a tree. Objective: if there s something simple that the hidden layer is doing, should be able to spot it. Illustration: CART analysis of one hidden node, shows that the node implements OCP-Phar quite well.
24 Statistical analysis Correlation of hidden node activation with violation of OCP Some represent OCP-Place in one node, others in two Consistent with results from CART analysis Often, strength of correlations and overlaps are interesting (i.e. reflect a pattern in the data) 24
25 Correlation example Labial stands alone strongly, coronal & dorsal more overlapping, coronal weak Consistent with lexical data CLS48 25
26 Summary Novel findings Learning with actual roots of Arabic and noise provides feedback for a phonotactic pattern grammar Behavior is qualitatively parallel to human intuitions of the OCP Hidden nodes have a symbolic interpretation, roughly corresponding to feature-specific OCP constraints (robust across reasonable number of nodes) 26
27 Limitations Only works with a fixed root structure, analogous to limitations of TRACE (though recurrent network versions have also been implemented) Modeler sets limited number of hidden nodes to force generalization (too few can t learn, too many overfit to lexicon) Model doesn t actually relate directly to psycholinguistic processes: Assessor node not an input-output processor 27
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