Machine Comprehension

By Dushyant Kumar. 2018

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension (2017, methodology): “This is similar to previous entity-centric classifiers for QA (Chen et al., 2016; Wang et al., 2015), and uses context and Wikipedia catalog based features.”
Improving Machine Reading Comprehension with General Reading Strategies (2018, methodology): “…answering by randomly reordering words or shufﬂing sentences (Ding and Zhou, 2018; Li and Zhou, 2018) or generating questions through paraphrasing (Yang et al., 2017; Yuan et al., 2017), which require a large amount of training data or limited by the number of training instances (Yu et al., 2018).”