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Applying the Transformer to Character-level Transduction ...
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Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction ...
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Applying the Transformer to Character-level Transduction
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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
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Do Explicit Alignments Robustly Improve Multilingual Encoders? ...
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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The Paradigm Discovery Problem ...
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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. ... : Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ...
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URL: https://dx.doi.org/10.3929/ethz-b-000462310 http://hdl.handle.net/20.500.11850/462310
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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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Emerging Cross-lingual Structure in Pretrained Language Models ...
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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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