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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
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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4
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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5
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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6
Including Signed Languages in Natural Language Processing ...
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7
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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8
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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9
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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10
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ...
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11
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors ...
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12
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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13
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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14
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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15
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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16
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.181 Abstract: Despite pre-trained language models have proven useful for learning high-quality semantic representations, these models are still vulnerable to simple perturbations. Recent works aimed to improve the robustness of pre-trained models mainly focus on adversarial training from perturbed examples with similar semantics, neglecting the utilization of different or even opposite semantics. Different from the image processing field, the text is discrete and few word substitutions can cause significant semantic changes. To study the impact of semantics caused by small perturbations, we conduct a series of pilot experiments and surprisingly find that adversarial training is useless or even harmful for the model to detect these semantic changes. To address this problem, we propose Contrastive Learning with semantIc Negative Examples (CLINE), which constructs semantic negative examples unsupervised to improve the robustness under semantically ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25504-cline-contrastive-learning-with-semantic-negative-examples-for-natural-language-understanding
https://dx.doi.org/10.48448/zn7h-g359
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17
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy ...
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18
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation ...
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19
Automated Concatenation of Embeddings for Structured Prediction ...
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20
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus ...
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