42 citations · 67 across the 11 of their papers we have counts for
9 papers
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
Chen Zhang, Jiuheng Lin, Xiao Liu +3
The multi-answer phenomenon, where a question may have multiple answers scattered in the document, can be well handled by humans but is challenging enough for machine reading compr…
Contrastive Hierarchical Discourse Graph for Scientific Document Summarization
Haopeng Zhang, Xiao Liu, Jiawei Zhang
The extended structural context has made scientific paper summarization a challenging task. This paper proposes CHANGES, a contrastive hierarchical graph neural network for extract…
The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code
Xiao Liu, Da Yin, Chen Zhang +2
Causal reasoning, the ability to identify cause-and-effect relationship, is crucial in human thinking. Although large language models (LLMs) succeed in many NLP tasks, it is still…
DiffuSum: Generation Enhanced Extractive Summarization with Diffusion
Haopeng Zhang, Xiao Liu, Jiawei Zhang
Extractive summarization aims to form a summary by directly extracting sentences from the source document. Existing works mostly formulate it as a sequence labeling problem by maki…
Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
Xiao Liu, Jian Zhang, Heng Zhang +2
Compared with standard text, understanding dialogue is more challenging for machines as the dynamic and unexpected semantic changes in each turn. To model such inconsistent semanti…
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Zhenyu Hou, Yufei He, Yukuo Cen +4
Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world g…