Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity (2021.emnlp-main)
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| Challenge: | Multi-task auxiliary learning uses a set of relevant auxiliary tasks to improve performance of a primary task. |
| Approach: | They propose a time-efficient sampling method to select the most beneficial sub-datasets from the auxiliary tasks to achieve efficient multi-task auxiliary learning. |
| Outcome: | The proposed method significantly outperforms random sampling and ST-DNN on three benchmark datasets. |
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| Challenge: | Existing methods to train multi-task models with auxiliary tasks are limited by the number of combinations and the importance of each auxiliary task is not known a priori. |
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| Challenge: | Multi-task learning and self-training are two common ways to improve a machine learning model’s performance in settings with limited training data. |
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| Challenge: | Multitask learning and transfer learning are techniques to overcome data scarcity . finding suitable auxiliary datasets for multitask learning is a trial-and-error approach . |
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| Challenge: | Multi-task learning is an inductive transfer mechanism that leverages information from related tasks to improve the primary model's generalization performance. |
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Dynamic Sampling Strategies for Multi-Task Reading Comprehension (2020.acl-main)
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| Challenge: | Using a neural network, large language models can be trained on multiple tasks, allowing them to perform tasks efficiently. |
| Approach: | They propose a framework that leverages a neural network to select the best dataset combinations for enhancing multi-task learning (MTL) They propose to iteratively refine the selection, greatly improving efficiency while being model-, dataset-, and domain-independent. |
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| Challenge: | Multi-task learning requires annotating the same text with multiple annotation schemes, which can be costly and laborious. |
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What to Pre-Train on? Efficient Intermediate Task Selection (2021.emnlp-main)
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| Challenge: | Existing methods for fine-tuning intermediate tasks are inefficient and expensive. |
| Approach: | They propose to use a set of 42 intermediate and 11 target English classification, multiple choice, question answering, and sequence tagging tasks to identify the best settings for intermediate transfer learning. |
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| Challenge: | Existing approaches to service account retrieval have limited human annotation, resulting in labor-intensive and time-consuming tasks. |
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