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Morphological Processing of Low-Resource Languages: Where We Are and What's Next ...
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Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability ...
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Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages ...
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How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
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{PROST}: {P}hysical Reasoning about Objects through Space and Time ...
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Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
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AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ...
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CLiMP: A Benchmark for Chinese Language Model Evaluation ...
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Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
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In: Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas. Edited by: Mager, Manuel; Oncevay, Arturo; Rios, Annette; Meza Ruiz, Ivan Vladimir; Palmer, Alexis; Neubig, Graham; Kann, Katharina (2021). Online: Association for Computational Linguistics. (2021)
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Learning to Learn Morphological Inflection for Resource-Poor Languages ...
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Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings ...
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Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge ...
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Abstract:
How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to-sequence architecture, which we train on different combinations of eight source and three target languages. A detailed analysis of the model outputs suggests the following conclusions: (i) if source and target language are closely related, acquisition of the target language's inflectional morphology constitutes an easier task for the model; (ii) knowledge of a prefixing (resp. suffixing) language makes acquisition of a suffixing (resp. prefixing) language's morphology more challenging; and (iii) surprisingly, a source language which exhibits an agglutinative morphology simplifies learning of a second language's inflectional morphology, independent of their relatedness. ... : SCiL 2020 ...
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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.1910.05456 https://arxiv.org/abs/1910.05456
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