| Challenge: | Existing methods for generating training data for semantic parsing are slow and expensive. |
| Approach: | They propose active learning for "overnight" and "natural language" parsing with a logical form . they propose several active learning strategies for overnight data collection . |
| Outcome: | The proposed approach reduces the cost of training data for deep parsing tasks by reducing the number of crowd workers required. |
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Active Learning for Multilingual Semantic Parser (2023.findings-eacl)
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| Challenge: | Existing multilingual semantic parsing datasets are limited in translation effort due to data imbalance. |
| Approach: | They propose a first active learning procedure for multilingual semantic parsing (AL-MSP) it selects only a subset from existing datasets to be translated, they propose . |
| Outcome: | The proposed method significantly reduces translation costs with ideal selection methods. |
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning (2023.acl-long)
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| Challenge: | Prior studies have focused on translating utterances from high-resource languages to low-resourced languages. |
| Approach: | They propose an active learning approach that exploits the strengths of both human and machine translations by iteratively adding small batches of human translations into the machine-translated training set. |
| Outcome: | The proposed approach reduces errors and bias in the translated data, resulting in higher parser accuracies than the current model trained on machine translations. |
Investigating Multi-source Active Learning for Natural Language Inference (2023.eacl-main)
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| Challenge: | Recent studies often assume that training and test data are drawn from the same distribution. |
| Approach: | They propose to apply active learning to unlabelled data pools to test for learning and generalisation. |
| Outcome: | The proposed strategies outperform random selection and outperformed hard-to-learn data on the task of natural language inference. |
An Imitation Game for Learning Semantic Parsers from User Interaction (2020.emnlp-main)
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| Challenge: | Existing methods for learning semantic parsers are expensive and tedious . despite the widespread applications, bootstrapping and fine-tuning is tedious a task . |
| Approach: | They propose an alternative method for learning semantic parsers directly from users . they propose an annotation-efficient imitation learning algorithm that iteratively collects new datasets . |
| Outcome: | The proposed method is cost-effective and shows promising performance on the text-to-SQL problem. |
Learning from Executions for Semantic Parsing (2021.naacl-main)
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| Challenge: | Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs. |
| Approach: | They propose to encourage a parser to generate executable programs for unlabeled NL utterances. |
| Outcome: | The proposed training objectives outperform conventional methods on Overnight and GeoQuery. |
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (2021.eacl-main)
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Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko
| Challenge: | Annotating training data for sequence tagging of texts is usually very time-consuming . active learning can help to reduce the amount of annotation required to train a good model by multiple times . |
| Approach: | They are the first to thoroughly investigate active learning and transfer learning for natural language processing . they propose to combine active learning with active learning to improve model acquisition . |
| Outcome: | The proposed combination of active learning and Bayesian uncertainty estimation improves performance and reduces obstacles for applying it in practice. |
A Survey of Active Learning for Natural Language Processing (2022.emnlp-main)
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| Challenge: | Existing literature surveys on active learning for NLP are too specific or too general, covering deep active learning. |
| Approach: | They propose to use active learning to improve model learning and annotation cost for NLP problems. |
| Outcome: | The proposed approach is based on a large dataset of data-driven machine learning models. |
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study (D18-1)
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| Challenge: | Existing studies on Active Learning (AL) for natural language processing have limited data requirements. |
| Approach: | They propose a Bayesian active learning approach that reduces deep learning's data dependence by comparing models and acquisition functions. |
| Outcome: | The proposed approach outperforms i.i.d. baselines and is more efficient than other approaches. |
Sampling Bias in Deep Active Classification: An Empirical Study (D19-1)
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| Challenge: | Existing studies on active learning identify sampling bias in large datasets . cost and time needed for labeling and model training are bottlenecks preventing new and/or better models from being trained . |
| Approach: | They propose to use active learning to identify representative data samples for training . they propose to create tiny datasets that can be used for cheap training if needed . |
| Outcome: | The proposed model outperforms the state-of-the-art on active text classification using small representative datasets with active learning. |
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey (2026.eacl-long)
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| Challenge: | a longstanding strategy to reduce annotation costs is active learning . data annotation is expected to remain important and active learning to stay relevant . |
| Approach: | They conduct an online survey to assess the perceived relevance of data annotation and active learning . they propose a strategy to reduce annotation costs using active learning, an iterative process . |
| Outcome: | The proposed strategies reduce setup complexity and uncertainty cost while maintaining model performance. |