Learning Opaque and Transparent Interactions in Harmonic Serialism
Bibliographic Data
| ID | 19572804 |
|---|---|
| Authors | Gaja Jarosz (0000-0002-8606-4704, University of Massachusetts Amherst, corresponding author) |
| Year | 2016 |
| Volume | 3 |
| Publication date | 2016-06-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Proceedings of the Annual Meetings on Phonology (JOURNAL) |
| Journal identifiers | ISSN: 2377-3324 • E-ISSN: 2377-3324 |
| Publisher | Linguistic Society of America (PUBLISHER • US) |
| DOI | 10.3765/amp.v3i0.3671 |
| OpenAlex | W2463693331 |
| Language | EN |
| Citations received | 8 |
| References cited | 6 |
This paper presents initial modeling results comparing the relative learnability of four basic types of process interactions: bleeding, feeding, counterfeeding and counterbleeding. The principle finding is that the learning model does not predict an absolute hierarchy of learning difficulty based on the type of process interaction involved. Rather, relative learning difficulty depends on numerous interacting factors, including how evidence for the target language is quantitatively distributed in the learning data, how distant the target language is from the initial grammar, and what alternative hypotheses the learner must rule out during learning. The model predicts that under some circumstances transparent orderings (Kiparsky 1971) are easier to learn but that under different conditions, orderings that maximize process utilization (Kiparsky 1968) are learned more easily. The model also predicts that different aspects of interactions can be difficult to learn under different circumstances: in some cases it is the orderings themselves that are difficult to learn, while in others, the model is slow to learn one of the processes involved in an interaction. The discussion relates these learning difficulties to Kiparsky’s (1968, 1971) discussion of diachronic rule reordering and rule loss and highlights directions for further research
Grammar · Hierarchy · Learnability · Linguistics · Natural language processing · Computer Science · Linguistic Studies and Language Acquisition · Natural Language Processing Techniques · Phonetics and Phonology Research · Artificial Intelligence
Analyzing opacity with contextual faithfulness constraints
Bedouin Arabic multiple opacity with indexed constraints in Parallel OT
Modeling the Acquisition of Phonological Interactions
Learning biases in opaque interactions
Input Strictly Local opaque maps
Learning opacity in Stratal Maximum Entropy Grammar
Opaque distributional generalisations in Tundra Nenets
Computational Modeling of Phonological Learning
| Citations per year | 0.89 |
|---|---|
| Citation span | 2017 - 2021 (5) |
| Highly cited | No |