| Challenge: | Argument component identification is difficult for trained annotators to perform in a new domain or to develop new AM tasks. |
| Approach: | They investigate whether multi-task learning can improve performance on AM problems . they found that MTL performs particularly well when little training data is available for the main task . |
| Outcome: | The proposed approach performs better when little training data is available for the main task, a common scenario in AM. |
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| Challenge: | Multi-task learning is a popular approach in natural language processing because of its commonalities and differences. |
| Approach: | They propose to summarize recent advances in multi-task learning methods based on their task relatedness into two general multi-step training methods. |
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Multi-Task Learning for Sequence Tagging: An Empirical Study (C18-1)
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| Challenge: | Existing work on "pairwise" MTL has been validated in sequence tagging but key issues remain about its effectiveness. |
| Approach: | They propose three general multi-task learning approaches on 11 sequence tagging tasks. |
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End-to-end Argument Mining with Cross-corpora Multi-task Learning (2022.tacl-1)
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| Challenge: | Argument(ation) mining is a task of identifying argument structure from text . lack of training data makes it difficult to train models based on limited data sets. |
| Approach: | They propose an end-to-end cross-corpus argument mining method that uses auxiliary argument mining corpora to train models. |
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Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models (2025.findings-naacl)
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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. |
| Outcome: | The proposed framework iteratively refines the selection, greatly improving efficiency, while being model-, dataset-, and domain-independent. |
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP (2023.acl-long)
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| Challenge: | Multi-task learning (MTL) is a machine learning paradigm where multiple learning tasks are optimized simultaneously, exploiting commonalities and differences across them. |
| Approach: | They propose a parameter-efficient MTL architecture to improve task aggregation and to include loosely related skills from multiple datasets. |
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A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding (2026.findings-acl)
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| Challenge: | a novel multi-task learning framework for domain-specific natural language understanding tasks addresses these limitations by combing multiple tasks into a single framework. |
| Approach: | They propose a multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. |
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Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning (D19-61)
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| Challenge: | Existing approaches to multi-task learning take advantage of transfer among tasks . generative reconstruction of the observations is not included in the standard framework . |
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MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
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| Challenge: | Existing task weighting methods assign weights only based on training losses, while ignoring the gap between the training loss and generalization loss. |
| Approach: | They propose a task weighting algorithm which automatically weights the tasks via a learning-to-learn paradigm and a multi-task text classification paradigm. |
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Multi-task Active Learning for Pre-trained Transformer-based Models (2022.tacl-1)
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| Challenge: | Multi-task learning requires annotating the same text with multiple annotation schemes, which can be costly and laborious. |
| Approach: | They propose to use multi-task active learning paradigm to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. |
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TARGER: Neural Argument Mining at Your Fingertips (P19-3)
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Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
| Challenge: | Argumentation is a multi-disciplinary field that extends from philosophy and psychology to linguistics as well as to artificial intelligence. |
| Approach: | They propose to use TARGER to tagging arguments in free text and keyword-based retrieval of arguments from a web-scale corpus. |
| Outcome: | The proposed framework can be used without any reproducibility effort on the user's side and is easily portable to other domains and use cases. |