Web-based Question Answering: Revisiting AskMSR
By Chen-Tse Tsai, Wen-tau Yih, C. Burges, Scott Yih. 2015
12 citations recorded by the source corpus.
Subfields: Information Retrieval & Search, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Retrieval & Search
- Jason Aaron Edward Weston
- Wen-tau Yih
- Hannaneh Hajishirzi
- Alexander H. Miller
- William W. Cohen
- Ming-Wei Chang
- Antoine Bordes
- Danqi Chen
In Natural Language Processing
- Jason Aaron Edward Weston
- Wen-tau Yih
- Mohit Bansal
- Jianfeng Gao
- Christopher D. Manning
- Luke S. Zettlemoyer
- Kyunghyun Cho
- Hannaneh Hajishirzi
Citations in the corpus
- Key-Value Memory Networks for Directly Reading Documents (2016, background): “Even though standard pipeline QA systems like AskMR (Banko et al. , 2002) have been recently re-visited (Tsai et al. , 2015), the best published results on T REC QA and W IKI QA have been obtained by either convolutional neural networks (Santos et al. , 2016; Yin and Schütze, 2015; Wang et al. ,…”
- Open Domain Question Answering via Semantic Enrichment (2015, methodology): “With these design changes and other enhancements detailed in [39], AskMSR+ increases MRR by roughly 25% on the TREC dataset, compared to the original AskMSR system, and is thus a solid baseline.”