SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling (N19-1)
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| Challenge: | Multi-task learning (MTL) has been studied for sequence labeling tasks . auxiliary tasks are selected specifically to improve performance of a target task . |
| Approach: | They propose a shared-cell long-short-term memory cell which contains shared parameters that can learn from all tasks and task-specific parameters that could learn task-related information. |
| Outcome: | The proposed model can learn from all tasks and task-specific parameters. |
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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. |
| Outcome: | The proposed approaches improve on 11 sequence tagging tasks. |
Sequence Labeling Parsing by Learning across Representations (P19-1)
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| Challenge: | Constituency and dependency parsing are the main abstractions for representing syntactic structure of sentences . constituency parsers are considered disjointed tasks, and their improvements have been obtained separately. |
| Approach: | They propose to add auxiliary loss to constituency parsing paradigms and explore a model that parses both paradigms at no cost. |
| Outcome: | The proposed model outperforms single-task models by 1.05 F1 points and 0.62 UAS points for constituency parsing and dependency parsers. |
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks (2020.acl-main)
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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 . |
| Approach: | They propose to automatically assess the similarity of sequence tagging datasets to identify beneficial auxiliary data for MTL or TL setups. |
| Outcome: | The proposed methods can compute similarity between two sequence tagging datasets . they show that the same measures correlate with the change in test score of the auxiliary dataset . |
Multi-Task Representation Alignment on Language Understanding: A Mutual Information Perspective (2026.acl-long)
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| Challenge: | Existing approaches to multitask learning fail to address task interference issues . Existing methods focus on task balancing or probabilistic modeling but fail to learn sufficient representations for all target tasks. |
| Approach: | They propose a multi-task representation alignment framework to achieve task-specific alignment and self-alignment on shared representations from a mutual information perspective. |
| Outcome: | The proposed framework outperforms 13 representative MTL methods under label-noisy and data-constrained conditions. |
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods (2023.eacl-main)
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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. |
| Outcome: | The proposed methods summarize the tasks and discuss future directions. |
Multi-Cell Compositional LSTM for NER Domain Adaptation (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a challenging but practical problem. |
| Approach: | They propose a multi-cell compositional LSTM structure for multi-task learning . they model each entity type using a separate cell state . |
| Outcome: | Empirical results show that the proposed method outperforms multi-task learning methods and achieves the best results. |
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. |
| Outcome: | The proposed framework surpasses conventional multi-task learning approaches in performance. |
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) requires large amounts of bilingual data to learn a translation model with reasonable quality. |
| Approach: | They propose to extend recurrent units with multiple "blocks" along with a trainable "routing network" this allows for adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. |
| Outcome: | Empirical evaluations of two low-resource translation tasks show +1 BLEU score improvements compared to strong baselines. |
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 . |
| Approach: | They propose to use a syntactically-oblivious pooling encoder and pre-trained word embeddings to improve sentence-level representations. |
| Outcome: | The proposed techniques yield similar performance on a universe of task combinations while reducing training time and model size. |
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. |