activity
20182022
most citedKnowledge Base Question Answering: A Semantic Parsing Perspective

15 citations · 40 across the 13 of their papers we have counts for

collaborators

19 papers

cs.CL2022

DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data

Xiao Li, Yin Zhu, Sichen Liu +3

Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community. To generate an executable reasoning program co…

cs.CL202215 cited

Knowledge Base Question Answering: A Semantic Parsing Perspective

Yu Gu, Vardaan Pahuja, Gong Cheng +1

Recent advances in deep learning have greatly propelled the research on semantic parsing. Improvement has since been made in many downstream tasks, including natural language inter…

cs.AI202211 cited

Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding

Dongzhuoran Zhou, Baifan Zhou, Jieying Chen +3

Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important appr…

cs.CV20225 cited

Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

Chunbo Lang, Binfei Tu, Gong Cheng +1

Few-shot segmentation, which aims to segment unseen-class objects given only a handful of densely labeled samples, has received widespread attention from the community. Existing ap…

cs.CV2022

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

Chunbo Lang, Gong Cheng, Binfei Tu +1

Recently few-shot segmentation (FSS) has been extensively developed. Most previous works strive to achieve generalization through the meta-learning framework derived from classific…

cs.CL20221 cited

AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension

Xiao Li, Gong Cheng, Ziheng Chen +2

Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reas…