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20162023
most citedVLP: A Survey on Vision-Language Pre-training

271 citations · 460 across the 22 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CL2023★ 5 cited

A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction

Zefa Hu, Ziyi Ni, Jing Shi +2

This paper focuses on term-status pair extraction from medical dialogues (MD-TSPE), which is essential in diagnosis dialogue systems and the automatic scribe of electronic medical…

cs.CL2023★ 22 cited

X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages

Feilong Chen, Minglun Han, Haozhi Zhao +4

Large language models (LLMs) have demonstrated remarkable language abilities. GPT-4, based on advanced LLMs, exhibits extraordinary multimodal capabilities beyond previous visual l…

cs.AI2023

Mixture of personality improved Spiking actor network for efficient multi-agent cooperation

Xiyun Li, Ziyi Ni, Jingqing Ruan +4

Adaptive human-agent and agent-agent cooperation are becoming more and more critical in the research area of multi-agent reinforcement learning (MARL), where remarked progress has…

eess.AS2023

VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition

Ziyi Ni, Minglun Han, Feilong Chen +4

Enhancing automatic speech recognition (ASR) performance by leveraging additional multimodal information has shown promising results in previous studies. However, most of these wor…

cs.CL2023

Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding

Zefa Hu, Xiuyi Chen, Haoran Wu +5

Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems. However, the lack of sufficien…

cs.CL2023

Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation

Minglun Han, Feilong Chen, Jing Shi +2

Large-scale pre-trained language models (PLMs) have shown great potential in natural language processing tasks. Leveraging the capabilities of PLMs to enhance automatic speech reco…