activity
20202023
most citedModeling Voting for System Combination in Machine Translation

5 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2023★ 2 cited

Unified Language Representation for Question Answering over Text, Tables, and Images

Bowen Yu, Cheng Fu, Haiyang Yu +2

When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data. Previous approaches to this problem have f…

cs.CL2023★ 2 cited

Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs

Yeqin Zhang, Haomin Fu, Cheng Fu +3

Multi-document grounded dialogue systems (DGDS) belong to a class of conversational agents that answer users' requests by finding supporting knowledge from a collection of document…

cs.CL2022★ 3 cited

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

Zhenyu Zhang, Bowen Yu, Haiyang Yu +6

Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to compre…

cs.CL2022★ 5 cited

Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction

Tianshu Wang, Hongyu Lin, Cheng Fu +6

Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, t…

cs.CL2020★ 5 cited

Modeling Voting for System Combination in Machine Translation

Xuancheng Huang, Jiacheng Zhang, Zhixing Tan +5

System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance. Although early statistical ap…