| Challenge: | Semantic typing aims at classifying tokens into semantic categories such as relations, entity types, and event types. |
| Approach: | They propose a unified framework for semantic typing that captures label semantics by projecting both inputs and labels into a joint semantic embedding space. |
| Outcome: | The proposed framework achieves strong performance across three semantic typing tasks. |
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| Challenge: | Existing work treats labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential label information. |
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Multitask Parsing Across Semantic Representations (P18-1)
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| Challenge: | UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data. |
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Generalizing Natural Language Analysis through Span-relation Representations (2020.acl-main)
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| Challenge: | a large number of natural language processing tasks are generated with specially designed architectures. |
| Approach: | They propose to represent a wide variety of tasks in a single unified format . they perform extensive experiments to demonstrate benefits of multi-task learning . |
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| Challenge: | Ultra-fine entity typing is a task of inferring the semantic types from a large set of fine-grained candidates that apply to a given entity mention. |
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| Challenge: | Existing methods for intent detection and slot filling decoders could result in misaligned predictions for both tasks. |
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
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Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond (2024.findings-acl)
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| Challenge: | Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders their adaptability to prompt-guided Large Language Models (LLMs). |
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Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction (2021.naacl-main)
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| Challenge: | Abstract Meaning Representation (IE) and Information Extraction (IE), both focus on extracting the main information from natural language texts. |
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A Unified Model for Reverse Dictionary and Definition Modelling (2022.aacl-short)
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| Challenge: | Using neural networks, we argue that both tasks can be learned and dealt with concurrently, based on the intuition that a word and its definition share the same meaning. |
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