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61
AND does not mean OR: Using Formal Languages to Study Language Models’ Representations ...
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62
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images ...
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63
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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64
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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65
DALC: the Dutch Abusive Language Corpus ...
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66
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition ...
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67
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes ...
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68
Attention-based Contextual Language Model Adaptation for Speech Recognition ...
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69
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
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70
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning ...
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71
LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification ...
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72
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions ...
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73
Evidence-based Factual Error Correction ...
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74
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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75
A DQN-based Approach to Finding Precise Evidences for Fact Verification ...
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76
Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event ...
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77
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification ...
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78
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.132 Abstract: We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset of 40,000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation. It includes 15,000 challenging perturbations and each hateful entry has fine-grained labels for the type and target of hate. Hateful entries make up 54% of the dataset, which is substantially higher than comparable datasets. We show that model performance is substantially improved using this approach. Models trained on later rounds of data collection perform better on test sets and are harder for annotators to trick. They also have better performance on HateCheck, a suite of functional tests for online hate detection. We provide the code, dataset and annotation guidelines for other researchers to use. ...
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/znsf-s209
https://underline.io/lecture/25441-learning-from-the-worst-dynamically-generated-datasets-to-improve-online-hate-detection
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79
2A: Sentiment Analysis, Stylistic Analysis, and Argument Mining #1 ...
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80
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation ...
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