Replication Data for: Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT-NLI
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Supervised machine learning is an increasingly popular tool for analysing large political text corpora. The main disadvantage of supervised machine learning is the need for thousands of manually annotated training data points. This issue is particularly important in the social sciences where most new research questions require the automation of a new task with new and imbalanced training data. This paper analyses how deep transfer learning can help address this challenge by accumulating ‘prior knowledge’ in algorithms. Pre-training algorithms like BERT creates representations of statistical language patterns (‘language knowledge’), and training on universal tasks like Natural Language Inference (NLI) reduces reliance on task-specific data (‘task knowledge’). We systematically show the benefits of transfer learning on a wide range of eigh' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["BERT","Text Analysis","transfer learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Supervised machine learning is an increasingly popular tool for analysing large political text corpora. The main disadvantage of supervised machine learning is the need for thousands of manually annotated training data points. This issue is particularly important in the social sciences where most new research questions require the automation of a new task with new and imbalanced training data. This paper analyses how deep transfer learning can help address this challenge by accumulating ‘prior knowledge’ in algorithms. Pre-training algorithms like BERT creates representations of statistical language patterns (‘language knowledge’), and training on universal tasks like Natural Language Inference (NLI) reduces reliance on task-specific data (‘task knowledge’). We systematically show the benefits of transfer learning on a wide range of eigh' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Supervised machine learning is an increasingly popular tool for analysing large political text corpora. The main disadvantage of supervised machine learning is the need for thousands of manually annotated training data points. This issue is particularly important in the social sciences where most new research questions require the automation of a new task with new and imbalanced training data. This paper analyses how deep transfer learning can help address this challenge by accumulating ‘prior knowledge’ in algorithms. Pre-training algorithms like BERT creates representations of statistical language patterns (‘language knowledge’), and training on universal tasks like Natural Language Inference (NLI) reduces reliance on task-specific data (‘task knowledge’). We systematically show the benefits of transfer learning on a wide range of eigh
BERT; Text Analysis; transfer learning