An Entailment-Based Approach to the QA4MRE Challenge
By Peter Clark, Philip Harrison, Xuchen Yao. 2012
12 citations recorded by the source corpus.
Subfields: Natural Language Processing, Question Answering & Reading Comprehension
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Ashish Sabharwal
- Tushar Khot
- Mohit Bansal
- Peter Clark
- Kevin Gimpel
- Luke S. Zettlemoyer
- Hannaneh Hajishirzi
- David A. McAllester
In Question Answering & Reading Comprehension
- Ashish Sabharwal
- Tushar Khot
- Harsh Trivedi
- Niranjan Balasubramanian
- Mohit Bansal
- Matthew Richardson
- Erin Renshaw
- C. Burges
Citations in the corpus
- MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, methodology): “The QA4MRE task also aims to evaluate machine reading systems through question answering (e.g., Clark et al., 2012).”
- Machine Comprehension with Syntax, Frames, and Semantics (2015, methodology): “Several comprehension tasks have been developed, including Re-media (Hirschman et al., 1999), CBC4kids (Breck et al., 2001), and the QA4MRE textual question answering tasks in the CLEF evaluations (Pe˜nas et al., 2011; Pe˜nas et al., 2013; Clark et al., 2012; Bhaskar et al., 2012).”
- Repurposing Entailment for Multi-Hop Question Answering Tasks (2019, background): “Entailment systems have been applied to questionanswering before but have only had limited success (Harabagiu and Hickl, 2006; Sacaleanu et al., 2008; Clark et al., 2012) in part because of the small size of the early entailment datasets (Dagan et al.”