RedTyp: A Database of Reduplication with Computational Models

Reduplication is a theoretically and typologically well-studied phenomenon, but there is no database of reduplication patterns which include explicit computational models. This paper introduces RedTyp, an SQL database which provides a computational resource that can be used by both theoretical and computational linguists who work on reduplication. It catalogs 138 reduplicative morphemes across 91 languages, which are modeled with 57 distinct finite-state machines. The finite-state machines are 2-way transducers, which provide an explicit, compact, and convenient representation for reduplication patterns, and which arguably capture the linguistic generalizations more directly than the more commonly used 1-way transducers for modeling natural language morphophonology

Strong Generative Capacity of Morphological Processes

Morphological processes are generally computable with 1-way finite-state transducers. However, we show that 1-way transducers do not capture the strong generative capacity of certain morphological analyses for more complex processes, including mobile affixation, infixation, and partial reduplication. As diagnostics for strong generative capacity, we use origin semantics and order-preservation. These analyze the input-output correspondences generated by finite-state transducers and their corresponding logical transductions. For some linguistic analyses of these complex processes, their strong generative capacity is matched by more expressive grammars, such as non-order-preserving transductions and their corresponding 2-way finite-state transducers

Probing RNN Encoder-Decoder Generalization of Subregular Functions using Reduplication

This paper examines the generalization abilities of encoder-decoder networks on a class of subregular functions characteristic of natural language reduplication. We find that, for the simulations we run, attention is a necessary and sufficient mechanism for learning generalizable reduplication. We examine attention alignment to connect RNN computation to a class of 2-way transducers

Finite-State Locality in Semitic Root-and-Pattern Morphology

This paper discusses the generative capacity required for Semitic root-and-pattern morphology. Finite-state methods effectively compute concatenative morpho-phonology, and can be restricted to Strictly Local functions. We extend these methods to consider non-concatenative morphology. We show that over such multi-input functions, Strict Locality is necessary and sufficient. We discuss some consequences of this generalization for linguistic theories of the morphological template

Learning Repetition, but not Syllable Reversal

Reduplication is common, but analogous reversal processes are rare, even though reversal, which involves nested rather than crossed dependencies, is less complex on the Chomsky hierarchy. We hypothesize that the explanation is that repetitions can be recognized when they match and reactivate a stored trace in short-term memory, but recognizing a reversal requires rearranging the input in working memory before attempting to match it to the stored trace. Repetitions can thus be recognized, and repetition patterns learned, implicitly, whereas reversals require explicit, conscious awareness. To test these hypotheses, participants were trained to recognize either a reduplication or a syllable-reversal pattern, and then asked to state the rule. In two experiments, above-chance classification performance on the Reversal pattern was confined to Correct Staters, whereas above-chance performance on the Reduplication pattern was found with or without correct rule-stating. Final proportion correct was positively correlated with final response time for the Reversal Correct Staters but no other group. These results support the hypothesis that reversal, unlike reduplication, requires conscious, time-consuming computation

Learning Local Phonological Processes

We present a learning algorithm for local phonological processes that relies on a restriction on the expressive power needed to compute phonological patterns that apply locally. Representing phonological processes as a functional mapping from an input to output form (an assumption compatible with either the SPE or OT formalism), the learner assumes the target process can be described with the functional counterpart to the Strictly Local (McNaughton and Papert 1971, Rogers and Pullum 2011) formal languages. Given a data set of input-output string pairs, the learner applies the two-stage grammatical induction procedure of 1) constructing a prefix tree representation of the input and 2) generalizing the pattern to words not found in the data set by merging states (Garcia and Vidal 1990, Oncina et al. 1993, Heinz 2007, 2009, de la Higuera 2010). The learner’s criterion for state merging enforces a locality requirement on the kind of function it can converge to and thereby directly reflects its own hypothesis space. We demonstrate with the example of German final devoicing, using a corpus of string pairs derived from the CELEX2 lemma corpus. The implications of our results include a proposal for how humans generalize to learn phonological patterns and a consequent explanation for why local phonological patterns have this property

Computing Vowel Harmony: The Generative Capacity of Search & Copy

Search & Copy (S&C) is a procedural model of vowel harmony in which underspecified vowels trigger searches for targets that provide them with features. In this paper, we seek to relate the S&C formalism with models of phonological locality proposed by recent work in the subregular program. Our goal is to provide a formal description, within the framework of mathematical linguistics, of the range of possible phonological transformations that admit an analysis within S&C. We show that used in its unidirectional mode, all transformations described by an S&C analysis can be modeled by tier-based input strictly local functions (TISL). This result improves the previous result of Gainor et al 2012, which showed that vowel harmony processes can be modeled by subsequential functions. However, non-TISL transformations can be given S&C descriptions in the following ways. Firstly, since TISL functions are not closed under composition, a non-TISL vowel harmony pattern may be obtained by applying two S&C rules sequentially. Secondly, when S&C is used in its bidirectional mode, it has the ability to describe transformations that cannot be modeled by finite-state functions