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Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming
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In: https://hal.archives-ouvertes.fr/hal-03590714 ; 2022 (2022)
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| 2 |
Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the HOLINET Knowledge Graph
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In: Proceedings of LREC 2022 ; https://hal-amu.archives-ouvertes.fr/hal-03655636 ; Proceedings of LREC 2022, Jun 2022, Marseille, France (2022)
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| 3 |
THE ROLE OF INTEGRATED ACTIVITIES TO DEVELOP WRITING COMPETENCE ...
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| 4 |
THE ROLE OF INTEGRATED ACTIVITIES TO DEVELOP WRITING COMPETENCE ...
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| 5 |
Introduction of Phonological Concepts in an Initial Teacher Education Literacy Unit
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In: Australian Journal of Teacher Education (2022)
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| 8 |
Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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| 9 |
Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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| 10 |
Morphology—A Gateway to Advanced Language: Meta-Analysis of Morphological Knowledge in Language-Minority Children ...
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| 11 |
Vocabulary knowledge predicts individual differences in the integration of visual and linguistic constraints
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| 12 |
Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
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In: Applied Sciences; Volume 12; Issue 4; Pages: 2185 (2022)
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Abstract:
Compared with the traditional few-shot task, the few-shot none-of-the-above (NOTA) relation classification focuses on the realistic scenario of few-shot learning, in which a test instance might not belong to any of the target categories. This undoubtedly increases the task’s difficulty because given only a few support samples, this cannot represent the distribution of NOTA categories in space. The model needs to make full use of the syntactic information and word meaning information learned in the pre-training stage to distinguish the NOTA category and the support sample category in the embedding space. However, previous fine-tuning methods mainly focus on optimizing the extra classifiers (on top of pre-trained language models (PLMs)) and neglect the connection between pre-training objectives and downstream tasks. In this paper, we propose the commonsense knowledge-aware prompt tuning (CKPT) method for a few-shot NOTA relation classification task. First, a simple and effective prompt-learning method is developed by constructing relation-oriented templates, which can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks. Second, external knowledge is incorporated into the model by a label-extension operation, which forms knowledgeable prompt tuning to improve and stabilize prompt tuning. Third, to distinguish the NOTA pairs and positive pairs in embedding space more accurately, a learned scoring strategy is proposed, which introduces a learned threshold classification function and improves the loss function by adding a new term focused on NOTA identification. Experiments on two widely used benchmarks (FewRel 2.0 and Few-shot TACRED) show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.
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Keyword:
commonsense knowledge-aware prompt tuning; few-shot none-of-the-above relation classification; pre-trained language models; scoring strategy
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URL: https://doi.org/10.3390/app12042185
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| 13 |
Deep Learning-Based End-to-End Language Development Screening for Children Using Linguistic Knowledge
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4651 (2022)
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| 14 |
The Impact of Student Teachers’ Pre-Existing Conceptions of Assessment on the Development of Language Assessment Literacy within an LTA Course
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In: Languages; Volume 7; Issue 1; Pages: 62 (2022)
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| 15 |
Preservice Teachers’ Knowledge and Attitudes toward Digital-Game-Based Language Learning
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In: Education Sciences; Volume 12; Issue 3; Pages: 182 (2022)
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| 16 |
The third wave of the IAB-BAMF-SOEP Survey of Refugees: Refugees are improving their German language skills and continue to feel welcome in Germany
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In: 1-2020 ; BAMF-Brief Analysis ; 18 (2022)
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| 17 |
Towards a theoretical understanding of word and relation representation
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| 18 |
Knowledge Building with Low Proficiency English Language Learners: Facilitating Metalinguistic Awareness and Scientific Understanding in Parallel
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[In Press] Quality and integrity in the translation of official documents
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