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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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Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.404 Abstract: We present a new probing dataset named PROST: Physical Reasoning about Objects Through Space and Time. This dataset contains 18,736 multiple-choice questions made from 14 manually curated templates, covering 10 physical reasoning concepts. All questions are designed to probe both causal and masked language models in a zero-shot setting. We conduct an extensive analysis which demonstrates that state-of-the-art pretrained models are inadequate at physical reasoning: they are influenced by the order in which answer options are presented to them, they struggle when the superlative in a question is inverted (e.g., most <-> least), and increasing the amount of pretraining data and parameters only yields minimal improvements. These results provide support for the hypothesis that current pretrained models' ability to reason about physical interactions is inherently limited by a lack of real world experience. By highlighting these ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Neural Network; Semantics
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URL: https://dx.doi.org/10.48448/ncjt-2s90 https://underline.io/lecture/26495-prost-physical-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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