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1
Automatic Error Type Annotation for Arabic ...
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2
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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3
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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4
Detecting Gender Bias using Explainability ...
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5
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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6
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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7
Contrastive Code Representation Learning ...
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8
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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9
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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10
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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11
Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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12
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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13
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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14
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.111/ Abstract: We investigate stereotypical information captured by pre-trained language models. We present a dataset comprising stereotypical attributes of a range of social groups and propose a method to elicit stereotypes encoded by pretrained language models in an unsupervised fashion. Moreover, we link the emergent stereotypes to their manifestation as basic emotions and demonstrate how our methods can be used to analyze emotion and stereotype shifts due to linguistic experience. Our experiments expose how attitudes towards different social groups vary across models and how quickly emotions and stereotypes can shift at the fine-tuning stage. ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/z296-1e12
https://underline.io/lecture/37741-stepmothers-are-mean-and-academics-are-pretentious-what-do-pretrained-language-models-learn-about-youquestion
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15
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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16
Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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17
Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
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18
Meta Distant Transfer Learning for Pre-trained Language Models ...
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19
How to Train BERT with an Academic Budget ...
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20
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media ...
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