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1
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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2
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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3
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models ...
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5
Including Signed Languages in Natural Language Processing ...
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6
Including Signed Languages in Natural Language Processing ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.570 Abstract: Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/x502-5y64
https://underline.io/lecture/25681-including-signed-languages-in-natural-language-processing
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7
Contrastive Explanations for Model Interpretability ...
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8
Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand? ...
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9
Measuring and Improving Consistency in Pretrained Language Models ...
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10
Aligning Faithful Interpretations with their Social Attribution ...
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11
Amnesic Probing: Behavioral Explanation With Amnesic Counterfactuals ...
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12
Data Augmentation for Sign Language Gloss Translation ...
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13
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent ...
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14
Asking It All: Generating Contextualized Questions for any Semantic Role ...
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15
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction ...
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16
Neural Extractive Search ...
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17
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction ...
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18
Ab Antiquo: Neural Proto-language Reconstruction ...
NAACL 2021 2021; Goldberg, Yoav; Meloni, Carlo. - : Underline Science Inc., 2021
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