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1. Automated tableau generation using SPOT (Syntax Prosody in Optimality Theory)

   https://doi.org/10.1515/lingvan-2017-0051  

   Bellik, Jennifer; Kalivoda, Nick (January 2019, Linguistics Vanguard)

   Abstract Much recent work on the syntax-prosody interface has been based in Optimality Theory. The typical analysis explicitly considers only a small number of candidates that could reasonably be expected to be optimal under some ranking, often without an explicit definition of GEN. Manually generating all the possible candidates, however, is prohibitively time-consuming for most input structures – the Too Many Candidates Problem. Existing software for OT uses regular expressions for automated generation and evaluation of candidates. However, regular expressions are too low in the Chomsky Hierarchy of language types to represent trees of arbitrary size, which are needed for syntax-prosody work. This paper presents a new computational tool for research in this area: Syntax-Prosody in Optimality Theory (SPOT). For a given input, SPOT generates all prosodic parses under certain assumptions about GEN, and evaluates them against all constraints in CON. This allows for in-depth comparison of the typological predictions made by different theories of GEN and CON at the syntax-prosody interface. 

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2. Overtly Headed XPs and Irish Syntax-Prosody Mapping

   https://doi.org/10.3765/amp.v9i0.4906  

   Kalivoda, Nick; Bellik, Jennifer (May 2021, Proceedings of the Annual Meetings on Phonology)

   null (Ed.)

   Analyses of Irish phonological phrasing (Elfner 2012 et seq.) have been influential in shaping Match Theory (Selkirk 2011), an OT approach to mapping syntactic to prosodic structure. We solve two constraint ranking paradoxes concerning the relative ranking of Match and StrongStart. Irish data indicate that while XPs with silent heads can fail to map to phonological phrases in certain circumstances, overtly headed XPs cannot. They also indicate that rebracketing due to the constraint StrongStart occurs only sentence-initially, contrary to predictions. We account for these puzzles by invoking Van Handel's (2019) Match constraint which sees only XPs with overt heads, and by positing a new version of StrongStart which only applies to material at the left edge of the intonational phrase. Our analysis is developed using the Syntax-Prosody in Optimality Theory application (SPOT) and OTWorkplace. 

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3. Match as Syntax-Prosody MAX/DEP: Prosodic Enclisis in English

   Ito, Junko; Mester, Armin (September 2019, English linguistics)

   This paper proposes to subsume Syntax-Prosody Match Theory under General Correspondence Theory, which distinguishes purely existential MAX/DEP constraints (requiring nothing but the existence of a correspondent in the output/input, which can be rather different from the input element) from IDENT and other faithfulness constraints. Exact correspondence (preservation of edges, no deletion, no insertion, uniqueness of mapping, order preservation, etc.) is enforced by Syntax-Prosody and Prosody-Syntax Alignment and by standard Faithfulness. The empirical topic is the impossibility of phrase-final enclisis in English (*I don't know where Tom's vs. Tom's here) and its proper explanation. 

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4. Recursive prosody is not finite-state

   Dolatian, Hossep; De Santo, Aniello; Graf, Thomas (January 2021, Proceedings of the Seventeenth SIGMORPHON Workshop on Computational Research)

   This paper investigates bounds on the generative capacity of prosodic processes, by focusing on the complexity of recursive prosody in coordination contexts in English (Wagner, 2010). Although all phonological processes and most prosodic processes are computationally regular string languages, we show that recursive prosody is not. The output string language is instead parallel multiple context-free (Seki et al., 1991). We evaluate the complexity of the pattern over strings, and then move on to a characterization over trees that requires the expressivity of multi bottom-up tree transducers. In doing so, we provide a foundation for future mathematically grounded investigations of the syntax-prosody interface. 

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5. On Scalable and Efficient Computation of Large Scale Optimal Transport

   Xie, Yujia; Chen, Minshuo; Jiang, Haoming; Zhao, Tuo; Zha, Hongyuan (June 2019, International Conference on Machine Learning)

   Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport). Specifically, we approximate the optimal transport plan by a pushforward of a reference distribution, and cast the optimal transport problem into a minimax problem. We then can solve OT problems efficiently using primal dual stochastic gradient-type algorithms. We also show that we can recover the density of the optimal transport plan using neural ordinary differential equations. Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior. SPOT also allows us to efficiently sample from the optimal transport plan, which benefits downstream applications such as domain adaptation. 

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