Papers with query strategy
Active Learning for Abstractive Text Summarization (2022.findings-emnlp)
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Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov
| Challenge: | Abstractive text summarization (ATS) requires a long document and short summaries. |
| Approach: | They propose a query strategy for AL in abstractive text summarization that uses uncertainty estimation to reduce model performance. |
| Outcome: | The proposed query strategy improves ROUGE and consistency scores for annotated datasets . it also increases the performance of the model, compared to passive annotation. |
A Multitask Active Learning Framework for Natural Language Understanding (2020.coling-main)
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| Challenge: | Existing studies have shown that relation information between intents and slots can improve the efficiency of active learning algorithms. |
| Approach: | They propose a multitask active learning framework that exploits relation information between sub-tasks provided by a joint model. |
| Outcome: | The proposed framework achieves competitive performance with less training data than baseline methods on all datasets. |