46 citations · 56 across the 7 of their papers we have counts for
7 papers
Distilling Transformers for Neural Cross-Domain Search
Colin B. Clement, Chen Wu, Dawn Drain +1
Pre-trained transformers have recently clinched top spots in the gamut of natural language tasks and pioneered solutions to software engineering tasks. Even information retrieval h…
Are Neural Ranking Models Robust?
Chen Wu, Ruqing Zhang, Jiafeng Guo +2
Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking…
Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix
Wenshuo Wang, Chen Wu, Liang Cheng +1
The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program ca…
Generating Bug-Fixes Using Pretrained Transformers
Dawn Drain, Chen Wu, Alexey Svyatkovskiy +1
Detecting and fixing bugs are two of the most important yet frustrating parts of the software development cycle. Existing bug detection tools are based mainly on static analyzers,…
Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers
Dawn Drain, Changran Hu, Chen Wu +2
We approach the important challenge of code autocompletion as an open-domain task, in which a sequence-to-sequence code generator model is enhanced with the ability to attend to re…
Learning to Truncate Ranked Lists for Information Retrieval
Chen Wu, Ruqing Zhang, Jiafeng Guo +3
Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically d…