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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.432 Abstract: Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. In this way, we find that crowdsourcing could be highly similar to domain adaptation, and then the recent advances of cross-domain methods can be almost directly applied to crowdsourcing. Here we take named entity recognition (NER) as a study case, suggesting an annotator-aware representation learning model that inspired by the domain adaptation methods which attempt to capture effective domain-aware features. We investigate both unsupervised and supervised crowdsourcing learning, assuming that no or only small-scale expert ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25861-crowdsourcing-learning-as-domain-adaptation-a-case-study-on-named-entity-recognition
https://dx.doi.org/10.48448/xzxf-pf88
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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 ...
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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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