Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering
By Haotian Zhang, Jinfeng Rao, Jimmy J. Lin, Mark D. Smucker. 2017
10 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jimmy J. Lin
- Wei Yang
- Zeynep Akkalyoncu Yilmaz
- Luchen Tan
- Ming Li
- Yuqing Xie
- Kun Xiong
- Shayne Longpre
In Information Systems
In Information Retrieval & Search
- Jimmy J. Lin
- Wei Yang
- Zeynep Akkalyoncu Yilmaz
- Ming Li
- Luchen Tan
- Yuqing Xie
- Kun Xiong
- Shayne Longpre
Citations in the corpus
- Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering (2019, methodology): “itive training examples, but previous work has shown the value of negative examples, specifically for QA (Zhang et al., 2017).”
- How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? (2020, background): “…have usually been observed where the augmentations are suited to the task: as with back-translation for machine translation (Edunov et al., 2018; Xia et al., 2019), or negative sampling for question answering and document retrieval (Zhang et al., 2017; Yang et al., 2019a; Xiong et al., 2020).”