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1
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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2
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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3
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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4
Tripartitions of the first person space (English speakers, Condition 1) ...
Maldonado, Mora. - : Open Science Framework, 2022
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5
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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6
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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7
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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8
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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9
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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10
Tripartitions of the first person space (Tamil speakers, Condition 1) ...
Maldonado, Mora. - : Open Science Framework, 2021
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11
Including Signed Languages in Natural Language Processing ...
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12
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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13
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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14
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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15
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ...
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16
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors ...
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17
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.276 Abstract: Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related ...
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/xht6-0j53
https://underline.io/lecture/25618-learnda-learnable-knowledge-guided-data-augmentation-for-event-causality-identification
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18
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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19
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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20
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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