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

Learning Interactions of Local and Non-Local Phonotactic Constraints from Positive Input

This paper proposes a grammatical inference algorithm to learn input-sensitive tier-based strictly local languages across multiple tiers from positive data only, when the locality of the tier-constraints and the tier-projection function is set to 2 (MITSL; De Santo and Graf, 2019). We conduct simulations showing that the algorithm succeeds in learning MITSL patterns over a set of artificial languages

Learning Nonlocal Phonotactics in a Strictly Piecewise Probabilistic Phonotactic Model

Phonotactic learning is a crucial aspect of phonological acquisition and has figured significantly in computational research in phonology (Prince & Tesar 2004). However, one persistent challenge for this line of research is inducing non-local co-occurrence patterns (Hayes & Wilson 2008). The current study develops a probabilistic phonotactic model based on the Strictly Piecewise class of subregular languages (Heinz 2010). The model successfully learns both segmental and featural representations, and correctly predicts the acceptabilities of the nonce forms in Quechua (Gouskova & Gallagher 2020)

Extending TSL to Account for Interactions of Local and Non-Local Constraints

Recent research in computational linguistics suggests that unbounded dependencies in phonotactics, morphology, and even syntax can all be captured by the class of Tier-based Strictly Local languages (TSL). Here, I explore a new class of subregular languages obtained by relaxing a particular constraint on the tier-projection mechanism of TSL grammars. I show how a small change in the definition of tier allows us to cover phonotactic patterns that escape the standard tier-based account, without losing any of the essential properties of TSL

Formal Restrictions On Multiple Tiers

In this paper, we use harmony systems with multiple feature spreadings as a litmus test for the possible configurations of items involved in certain dependence. The subregular language classes, and the class of tier-based strictly local (TSL) languages in particular, have shown themselves as a good fit for different aspects of natural language. It is also known that there are some patterns that cannot be captured by a single TSL grammar. However, no proposed limitations exist on tier alphabets of several cooperating TSL grammars. While theoretically possible relations among tier alphabets of several TSL grammars are containment, disjunction and intersection, the latter one appears to be unattested. Apart from presenting the typological overview, we discuss formal reasons that might explain such distribution

Action-Sensitive Phonological Dependencies

This paper defines a subregular class of functions called the tier-based synchronized strictly local (TSSL) functions. These functions are similar to the the tier-based input-output strictly local (TIOSL) functions, except that the locality condition is enforced not on the input and output streams, but on the computation history of the minimal subsequential finite-state transducer. We show that TSSL functions naturally describe rhythmic syncope while TIOSL functions cannot, and we argue that TSSL functions provide a more restricted characterization of rhythmic syncope than existing treatments within Optimality Theory.Comment: To appear in the Proceedings of the 16th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morpholog

Is Sour Grapes Learnable? A Computational and Experimental Approach

In this paper, I present results from simulations using three different maximum entropy phonotactic models (Hayes & Wilson, 2008; Moreton et al., 2017): one that can only represent Sour Grapes, one that can only represent standard, attested harmony, and one that has the expressive power to capture both patterns. I then present results from an experiment designed to test the predictions of these models and find that humans behave most like the model that can capture both generalizations—challenging the idea that Sour Grapes is categorically unlearnable

Multiple Wh-Movement is not Special: The Subregular Complexity of Persistent Features in Minimalist Grammars

Minimalist grammars have been criticized for their inability to analyze successive cyclic movement and multiple wh-movement in a manner that is faithful to the Minimalist literature. Persistent features have been proposed in the literature as a potential remedy (Stabler 2011, Laszakovits 2018). We show that not all persistent features are alike. The persistent features involved in multiple wh-movement do not increase subregular complexity, making this phenomenon appear very natural from the perspective of MGs. The persistent features in successive-cyclic movement, on the other hand, change the subregular nature of movement, favoring an alternative treatment along the lines of Kobele (2006

NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1