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To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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Applying the Transformer to Character-level Transduction ...
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Do RNN States Encode Abstract Phonological Alternations? ...
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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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Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models ...
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth; Mielke, Sabrina J.; Wu, Shijie; Ponti, Edoardo; Maudslay, Rowan Hall; Zmigrod, Ran; Valvoda, Josef; Toldova, Svetlana; Tyers, Francis; Klyachko, Elena; Yegorov, Ilya; Krizhanovsky, Natalia; Czarnowska, Paula; Nikkarinen, Irene; Krizhanovsky, Andrew; Pimentel, Tiago; Hennigen, Lucas Torroba; Kirov, Christo; Nicolai, Garrett; Williams, Adina; Anastasopoulos, Antonios; Cruz, Hilaria; Chodroff, Eleanor; Cotterell, Ryan; Silfverberg, Miikka; Hulden, Mans. - : arXiv, 2020
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Abstract:
A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems' ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed ... : 39 pages, SIGMORPHON ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2006.11572 https://arxiv.org/abs/2006.11572
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UniMorph 3.0: Universal Morphology
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In: Proceedings of the 12th Language Resources and Evaluation Conference (2020)
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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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RNN Classification of English Vowels: Nasalized or Not
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In: Proceedings of the Society for Computation in Linguistics (2019)
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On the Complexity and Typology of Inflectional Morphological Systems
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 327-342 (2019) (2019)
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Marrying Universal Dependencies and Universal Morphology ...
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On the Complexity and Typology of Inflectional Morphological Systems ...
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Sound Analogies with Phoneme Embeddings
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In: Proceedings of the Society for Computation in Linguistics (2018)
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Quantifying the Trade-off Between Two Types of Morphological Complexity
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In: Proceedings of the Society for Computation in Linguistics (2018)
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A Comparison of Feature-Based and Neural Scansion of Poetry ...
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