Generating Natural Language From Logic Expressions With Structural Representation

Wu, Xin Xin; Cai, Yi; Lian, Zetao; Leung, Ho-fung; Wang, Tao

doi:10.1109/taslp.2023.3263784

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2024

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Cited by 3 publications

References 42 publications

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TrsNet: A TRS-based deep learning network for carrier frequency offset estimation in 5G system

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TrsNet: A TRS-based deep learning network for carrier frequency offset estimation in 5G system

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Artificial Intelligence for Wireless Physical-Layer Technologies (AI4PHY): A Comprehensive Survey

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Pan

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Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model

yuan,

Cai,

Huang

2024

Proceedings of the 32nd ACM International Conference on Multimedia

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Joint Multimodal Entity-Relation Extraction (JMERE) is a challenging task that aims to extract entities and their relations from textimage pairs in social media posts. Existing methods for JMERE require large amounts of labeled data. However, gathering and annotating fine-grained multimodal data for JMERE poses significant challenges. Initially, we construct diverse and comprehensive multimodal few-shot datasets fitted to the original data distribution. To address the insufficient information in the few-shot setting, we introduce the Knowledge-Enhanced Cross-modal Prompt Model (KECPM) for JMERE. This method can effectively address the problem of insufficient information in the few-shot setting by guiding a large language model to generate supplementary background knowledge. Our proposed method comprises two stages: (1) a knowledge ingestion stage that dynamically formulates prompts based on semantic similarity guide ChatGPT generating relevant knowledge and employs self-reflection to refine the knowledge; (2) a knowledge-enhanced language model stage that merges the auxiliary knowledge with the original input and utilizes a transformerbased model to align with JMERE's required output format. We extensively evaluate our approach on a few-shot dataset derived from the JMERE dataset, demonstrating its superiority over strong baselines in terms of both micro and macro F 1 scores. Additionally, we present qualitative analyses and case studies to elucidate the effectiveness of our model.

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Generating Natural Language From Logic Expressions With Structural Representation

Cited by 3 publications

References 42 publications

TrsNet: A TRS-based deep learning network for carrier frequency offset estimation in 5G system

TrsNet: A TRS-based deep learning network for carrier frequency offset estimation in 5G system

Artificial Intelligence for Wireless Physical-Layer Technologies (AI4PHY): A Comprehensive Survey

Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model

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