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1
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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2
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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3
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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4
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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5
Including Signed Languages in Natural Language Processing ...
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6
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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7
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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8
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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9
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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10
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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11
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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12
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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13
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding ...
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14
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy ...
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15
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation ...
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16
Automated Concatenation of Embeddings for Structured Prediction ...
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17
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus ...
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18
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data ...
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19
Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.341 Abstract: With the rapid growth of social media in the past decade, the news are no longer controlled by just a few mainstream sources. Users themselves create large numbers of potentially fictitious rumours, necessitating automated veracity classification systems. Here we present a novel approach towards automatically classifying rumours circulating on Twitter with respect to their veracity. We use a model built on Variational Autoencoder which disentangles the informational content of a tweet from the manner in which the information is written. This is achieved by obtaining latent topic vectors in an adversarial learning setting using the auxiliary task of stance classification. The latent vectors learnt in this way are used to predict rumour veracity, obtaining state-of-the-art accuracy scores on the PHEME dataset. ...
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/xvn7-0f32
https://underline.io/lecture/26432-learning-disentangled-latent-topics-for-twitter-rumour-veracity-classification
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20
Scaling Within Document Coreference to Long Texts ...
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