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What transfers in morphological inflection? Experiments with analogical models ...
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Formalizing Inflectional Paradigm Shape with Information Theory
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In: Proceedings of the Society for Computation in Linguistics (2021)
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The Paradigm Discovery Problem
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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Abstract:
This work treats the paradigm discovery problem (PDP), the task of learning an inflectional morphological system from unannotated sentences. We formalize the PDP and develop evaluation metrics for judging systems. Using currently available resources, we construct datasets for the task. We also devise a heuristic benchmark for the PDP and report empirical results on five diverse languages. Our benchmark system first makes use of word embeddings and string similarity to cluster forms by cell and by paradigm. Then, we bootstrap a neural transducer on top of the clustered data to predict words to realize the empty paradigm slots. An error analysis of our system suggests clustering by cell across different inflection classes is the most pressing challenge for future work.
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URL: https://hdl.handle.net/20.500.11850/462310 https://doi.org/10.3929/ethz-b-000462310
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Interpreting Sequence-to-Sequence Models for Russian Inflectional Morphology
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Stop the Morphological Cycle, I Want to Get Off: Modeling the Development of Fusion
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Normalization may be ineffective for phonetic category learning ...
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Normalization may be ineffective for phonetic category learning
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Giving Good Directions: Order of Mention Reflects Visual Salience
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Giving Good Directions: Order of Mention Reflects Visual Salience
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POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process
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Where's Wally: the influence of visual salience on referring expression generation
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Structured generative models for unsupervised named-entity clustering
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