14 citations · 44 across the 10 of their papers we have counts for
11 papers
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning
Qian Jiang, Changyou Chen, Han Zhao +6
Contrastive loss has been increasingly used in learning representations from multiple modalities. In the limit, the nature of the contrastive loss encourages modalities to exactly…
Efficient and effective training of language and graph neural network models
Vassilis N. Ioannidis, Xiang Song, Da Zheng +6
Can we combine heterogenous graph structure with text to learn high-quality semantic and behavioural representations? Graph neural networks (GNN)s encode numerical node attributes…
DynaMaR: Dynamic Prompt with Mask Token Representation
Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam +4
Recent research has shown that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. Typically wh…
Multi-modal Alignment using Representation Codebook
Jiali Duan, Liqun Chen, Son Tran +4
Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusi…
Vision-Language Pre-Training with Triple Contrastive Learning
Jinyu Yang, Jiali Duan, Son Tran +6
Vision-language representation learning largely benefits from image-text alignment through contrastive losses (e.g., InfoNCE loss). The success of this alignment strategy is attrib…
Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning
Xuanli He, Iman Keivanloo, Yi Xu +4
Pre-training and then fine-tuning large language models is commonly used to achieve state-of-the-art performance in natural language processing (NLP) tasks. However, most pre-train…
Dialogue-oriented Pre-training
Yi Xu, Hai Zhao
Pre-trained language models (PrLM) has been shown powerful in enhancing a broad range of downstream tasks including various dialogue related ones. However, PrLMs are usually traine…
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
Junya Chen, Zhe Gan, Xuan Li +10
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years. However, these contrastive schemes are notoriously resource de…
Topic-Aware Multi-turn Dialogue Modeling
Yi Xu, Hai Zhao, Zhuosheng Zhang
In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances.…
On Leveraging the Visual Modality for Neural Machine Translation
Vikas Raunak, Sang Keun Choe, Quanyang Lu +2
Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics. Recently, Caglayan et al. posit that the obser…