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1
Predicting Declension Class from Form and Meaning
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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2
The Paradigm Discovery Problem
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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.
URL: https://hdl.handle.net/20.500.11850/462310
https://doi.org/10.3929/ethz-b-000462310
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3
A Tale of a Probe and a Parser
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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4
A Corpus for Large-Scale Phonetic Typology
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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5
Information-Theoretic Probing for Linguistic Structure
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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6
It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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