I consider myself a descriptive grammarian who wants to understand his data, and a computational linguist who loves puzzle solving. To that end, I use a mix of theoretical and computational tools to understand different areas of morphophonology. I have worked on different areas of morphophonology
You can find out more below. For my dissertation, I am working on the computational morphophonology of Armenian under the supervision of Dr. Jeff Heinz and Dr. Christina Bethin. Armenian is understudied but has morphophonological processes that are sensitive to multiple linguistic factors. I discover these factors in my thesis and I work out how computationally powerful morphophonology needs to be.
- 1 Morphophonology of Armenian
- 2 Computational phonology and morphophonology
- 3 Acoustic documentation of phonological prominence
Morphophonology of Armenian
Armenian is an understudied Indo-European language with an odd mix of typological properties. I focus on the various ways morphology and phonology interact within the word. I mostly focus on stress and stress-based phonological processes in Eastern and Western Armenian, with some excursions into neighboring endangered dialects.
Over the years, I have gathered data and developed theoretical models to capture:
- vowel reduction within sublexical prosodic constituents
- vowel reduction in cyclic word-formation and derivation
- vowel reduction and subregularities in syllable structure
- bracketing paradoxes in compounds
- phonologically and syntactically conditioned affix mobility (with Nikita Bezrukov)
To learn more these points, check out:
Computational phonology and morphophonology
Language is a pattern, and patterns must be computable. But, different patterns need different power. I focus on studying the computability of morphology and phonological from the perspective of Formal Language Theory and finite-state calculus.
Reduplication is a typologically common yet computational difficult process to model. Under the supervision of Dr. Jeffrey Heinz, I have explored the use of uncommonly used finite-state technology (2-way finite-state transducers) in handling the high computational complexity of reduplication. Using 2-way FSTs, we have:
- looked at its ability to generate the typology of reduplication
- discovered subclasses of 2-way FSTs that match the linguistic typology
- determined the learnability of these subclasses.
- shown how they are an insightful computational model of reduplication
- developed the RedTyp database on reduplication using 2-way FSTs
RedTyp can be accessed on our GitHub. To learn more about these points, check out:
-  CLS53 paper and slides
-  NAPhCX slides
- [2&3] ICGI paper (link) and slides
-  SIGMorPhon paper (link) and poster
-  SCiL paper (link) and slides
With Jonathan Rawski, I’ve recently looked at the computational power needed to model certain types of phonological processes that use multiple inputs using multi-tape transducers.
Templatic morphology in Semitic
Semitic templatic morphology or root-and-pattern morphology is classically studied as a process of combining multiple input morphemes. In collaboration with Jonathan Rawski, we discovered how the subregular hierarchy for single-tape finite-state transducers can be extended to multi-input functions like template formation. We formulated the class of local multi-input functions and multi-tape transducers
Tone and Tonal phonology
As an outgrowth of our work on Semitic morphology, Jonathan Rawski have extended the use of multi-tape transducers for tone and tonal phonology and autosegmental structure. As with templatic morphology, a large chunk tonal phonology can be computed with a restricted type of local multi-input functions and multi-tape transducers.
Formal definition of n-regular functions
Locality in multi-input functions is a significant computational generalization in templatic morphology and tone. In collaboration with Adam Jardine and Tadjou-N’Dine Mamadou Y., Jon Rawski and I are expanding and refining our initial formal definitions of locality in multi-input functions.
- Paper (TBA)
Formal language theory and neural networks
Formal language theory provides abstract but computible descriptions of natural language process. In collaboration with Max Nelson, Brandon Prickett, and Jonathan Rawski, we test how well neural networks can match the behavior of subregular functions and their finite-state transducers.
Our main case study so far is reduplication. Reduplication can be computed by different types of 1-way and 2-way finite-state transducers, and by different types of neural networks. When learning reduplication, we show that different parameters for a neural network correspond to different classes of finite-state transducers.
- SCiL paper and slides (TBA)
Tier-based computation in phonology
In collaboration with Yiding Hao and Samuel Andersson, we’ve looked at how vowel harmony patterns can be computed over tiers. We show that harmony patterns are locally computed over a projected vowel tier. The composition of multiple harmony rules is however non-local.
- AMP poster
Acoustic documentation of phonological prominence
As a (long-distance) member of Dr. Irene Vogel‘s Prosodic Typologies lab, I have helped the lab carry out a cross-linguistic acoustic documentation of phonological prominence. Because of methodological difficulties in making comparative analyses of acoustic work on stress, the lab’s goal is to use systematically use the same rigorous experimentation methodology in documenting the word-level and sentence-level prominence of both well-studied and under-studied languages.
Under the supervision of Dr. Vogel, I have carried out an acoustic documentation of prominence in Armenian. The results were included in our Interspeech 2017 proceeding.
I am currently working on expanding the project to include Uzbek and a couple of other languages.