43 citations · 68 across the 20 of their papers we have counts for
7 papers · 1 filter
Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining
Guanghao Zhu, Zeyu Liu, Zhitian Hou +10
Biomedical figures are explained not by captions alone but by body-text passages that discuss them. Yet current multimodal corpora typically reduce figures to isolated image-captio…
Measurement of LLM's Philosophies of Human Nature
Minheng Ni, Ennan Wu, Zidong Gong +6
The widespread application of artificial intelligence (AI) in various tasks, along with frequent reports of conflicts or violations involving AI, has sparked societal concerns abou…
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification
Libo Qin, Zhouyang Li, Wanxiang Che +2
In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the ex…
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
Libo Qin, Wanxiang Che, Yangming Li +2
In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the expl…
CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP
Libo Qin, Minheng Ni, Yue Zhang +1
Multi-lingual contextualized embeddings, such as multilingual-BERT (mBERT), have shown success in a variety of zero-shot cross-lingual tasks. However, these models are limited by h…
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
Minheng Ni, Haoyang Huang, Lin Su +6
We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre…
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Libo Qin, Minheng Ni, Yue Zhang +3
Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for each doma…