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| 101 |
How Well Do LSTM Language Models Learn Filler-gap Dependencies?
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 102 |
A split-gesture, competitive, coupled oscillator model of syllable structure predicts the emergence of edge gemination and degemination
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 103 |
Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 104 |
Learning Argument Structures with Recurrent Neural Network Grammars
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 105 |
MaxEnt Learners are Biased Against Giving Probability to Harmonically Bounded Candidates
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Abstract:
One of the major differences between MaxEnt Harmonic Grammar (Goldwater and Johnson, 2003) and Noisy Harmonic Grammar (Boersma and Pater, 2016) is that in MaxEnt harmonically bounded candidates are able to get some probability, whereas in most other constraint-based grammars they can never be output (Jesney, 2007). The probability given to harmonically bounded candidates is taken from other candidates, in some cases allowing Max- Ent to model grammars that subvert some of the universal implications that are true in NoisyHG (Anttila and Magri, 2018). Magri (2018) argues that the types of implicational universals that remain valid in MaxEnt are phonologically implausible, suggesting that Max- Ent overgenerates NoisyHG. However, recent work has shown that some of the possible grammars in a constraint based grammar may be unlikely to be observed because they are difficult to learn (Staubs, 2014; Stanton, 2016; Pater and Moreton, 2012; Hughto, 2019; O’Hara, 2021). Here, I show that grammars that give weight to harmonically bounded candidates are harder to learn than other grammars. With learnability applied, I claim that the typological predictions of MaxEnt and NoisyHG are in fact much more similar than they would seem based on the grammars alone.
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Keyword:
Computational Linguistics; computational phonology; harmonically bounded; learning bias; MaxEnt; noisy harmonic grammar; Phonetics and Phonology; phonological learning; Typological Linguistics and Linguistic Diversity; typological overgeneration
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URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1252&context=scil
https://scholarworks.umass.edu/scil/vol5/iss1/24
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| 106 |
Evaluating Structural Economy Claims in Relative Clause Attachment
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 107 |
A Model Theoretic Perspective on Phonological Feature Systems
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 108 |
Representing Multiple Dependencies in Prosodic Structures
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 109 |
Incremental Acquisition of a Minimalist Grammar using an SMT-Solver
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 110 |
Concurrent hidden structure & grammar learning
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 111 |
Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructions
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 112 |
Universal Dependencies and Semantics for English and Hebrew Child-directed Speech
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 113 |
Learning Input Strictly Local Functions: Comparing Approaches with Catalan Adjectives
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 114 |
Learning Constraints on Wh-Dependencies by Learning How to Efficiently Represent Wh-Dependencies: A Developmental Modeling Investigation With Fragment Grammars
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 115 |
Typological Implications of Tier-Based Strictly Local Movement
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 116 |
Parsing Early Modern English for Linguistic Search
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 117 |
When Classifying Arguments, BERT Doesn't Care About Word Order. Except When It Matters
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 118 |
Remodelling complement coercion interpretation
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 119 |
Inferring Inferences: Relational Propositions for Argument Mining
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 120 |
ANLIzing the Adversarial Natural Language Inference Dataset
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In: Proceedings of the Society for Computation in Linguistics (2022)
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