| Challenge: | Existing approaches to learning vector-space representations of text are multitask learning and language model pre-training. |
| Approach: | They propose a multi-task deep neural network (MT-DNN) that leverages cross-task data and incorporates a pre-trained bidirectional transformer language model. |
| Outcome: | The proposed model achieves state-of-the-art on ten NLU tasks and pushes the GLUE benchmark to 82.7% (2.2% absolute improvement) |
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Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao
| Challenge: | MT-DNN is an open-source natural language understanding toolkit . it allows researchers and developers to train customized deep learning models . |
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| Outcome: | The proposed model can significantly compress a large model without significant performance drop. |
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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Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks (D19-1)
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| Challenge: | Existing methods to learn general representations of text can achieve sub-optimal performance in low-resource scenarios. |
| Approach: | They propose to use language model pre-training and multi-task learning to learn robust representations but these methods can achieve sub-optimal performance in low-resource scenarios. |
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Deep Natural Language Feature Learning for Interpretable Prediction (2023.emnlp-main)
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| Challenge: | Using a small transformer language model, we can break down a complex task into a set of intermediary easier sub-tasks. |
| Approach: | They propose a method to break down a main task into a set of intermediary easier sub-tasks, which are formulated in natural language as binary questions related to the final target task. |
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LIMIT-BERT : Linguistics Informed Multi-Task BERT (2020.findings-emnlp)
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| Challenge: | Existing language models are usually trained on large amounts of unlabeled text data. |
| Approach: | They propose a multi-task language representations learning framework for multi-linguistics tasks by Multi-Task Learning. |
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Learning Language Specific Sub-network for Multilingual Machine Translation (2021.acl-long)
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| Challenge: | Multilingual neural machine translation models suffer from performance degradation when learning multiple languages. |
| Approach: | They propose to use LaSS to jointly train a single unified multilingual MT model. |
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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. |
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Deep Learning for Natural Language Inference (N19-5)
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
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AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020.aacl-demo)
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| Challenge: | AutoNLU is an on-demand cloud-based system that enables users to create and edit datasets and train and test different state-of-the-art NLU models. |
| Approach: | They introduce an on-demand cloud-based system that provides an easy-to-use interface . they build powerful keyphrase extraction models that achieve state-of-the-art results . |
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Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
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