4 citations · 5 across the 2 of their papers we have counts for
8 papers
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training
Zhou Zhang, Dongzeng Tan, Jiaan Wang +2
Emojis have gained immense popularity on social platforms, serving as a common means to supplement or replace text. However, existing data mining approaches generally either comple…
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning
Jiaan Wang, Jianfeng Qu, Kexin Wang +4
Knowledge-grounded dialogue (KGD) learns to generate an informative response based on a given dialogue context and external knowledge (\emph{e.g.}, knowledge graphs; KGs). Recently…
AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
Jingdan Zhang, Jiaan Wang, Xiaodan Wang +2
Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale M…
Unified Model Learning for Various Neural Machine Translation
Yunlong Liang, Fandong Meng, Jinan Xu +3
Existing neural machine translation (NMT) studies mainly focus on developing dataset-specific models based on data from different tasks (e.g., document translation and chat transla…
Towards Unifying Multi-Lingual and Cross-Lingual Summarization
Jiaan Wang, Fandong Meng, Duo Zheng +4
To adapt text summarization to the multilingual world, previous work proposes multi-lingual summarization (MLS) and cross-lingual summarization (CLS). However, these two tasks have…
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
Yuxuan Cao, Jiarong Xu, Carl Yang +5
In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Desp…