Stacking With Auxiliary Features
By Nazneen Rajani, Raymond J. Mooney. 2016
15 citations recorded by the source corpus.
Ensembling methods are well known for improving prediction accuracy. However, they are limited in the sense that they cannot discriminate among component models effectively. In this paper, we propose stacking with auxiliary features that learns to fuse relevant information from multiple systems to improve performance. Auxiliary features enable the stacker to rely on systems that not just agree on an output but also the provenance of the output. We demonstrate our approach on three very different and difficult problems -- the Cold Start Slot Filling, the Tri-lingual Entity Discovery and Linking and the ImageNet object detection tasks. We obtain new state-of-the-art results on the first two tasks and substantial improvements on the detection task, thus verifying the power and generality of our approach.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Nazneen Fatema Rajani and Raymond J. Mooney
- Nazneen Rajani
- Raymond J. Mooney
- Percy Liang
- Luke S. Zettlemoyer
- Mohit Bansal
- Xi Ye
- Jayant Krishnamurthy
Citations in the corpus
- Stacking with Auxiliary Features for Visual Question Answering (2018, methodology): “Stacking with Auxiliary Features (SWAF) (Rajani and Mooney, 2017) is a recent ensembling algorithm that learns to combine outputs of multiple systems using fea-2217 tures of the current problem as context.”
- Combining Supervised and Unsupervised Enembles for Knowledge Base Population (2016, background): “Using this new approach, we demonstrate new state-of-the-art results on two NIST KBP challenge tasks: Cold Start Slot-Filling (CSSF)1 and the Trilingual Entity Discovery and Linking (TEDL) (Ji et al., 2015).”
- Ensembling Visual Explanations for VQA (2017, methodology): “We first build an ensemble model that uses Stacking With Auxiliary Features (SWAF) [9] to combine outputs of the three component systems.”
- Using Explanations to Improve Ensembling of Visual Question Answering Systems (2017, methodology): “In this paper, we use Stacking with Auxiliary Features (SWAF) [Rajani and Mooney, 2017] to more effectively combine diverse VQA models.”