Paper Info
Title
Questionable Answers in Question Answering Research: Reproducibility and Variability of Published Results.
Abstract
Based on theoretical reasoning it has been suggested that the reliability of findings published in the scientific literature decreases with the popularity of a research field (Pfeiffer and Hoffmann, 2009). As we know, deep learning is very popular and the ability to reproduce results is an important part of science. There is growing concern within the deep learning community about the reproducibility of results that are presented.  In this paper we present a number of controllable, yet unreported, effects that can substantially change the effectiveness of a sample model, and thusly the reproducibility of those results. Through these environmental effects we show that the commonly held belief that distribution of source code is all that is needed for reproducibility is not enough. Source code without a reproducible environment does not mean anything at all. In addition the range of results produced from these effects can be larger than the majority of incremental improvement reported.
Year
Venue
Field
2018
TACL
Data science,Scientific literature,Question answering,Computer science,Popularity,Artificial intelligence,Natural language processing
DocType
Volume
Citations 
Journal
6
2
PageRank 
References 
Authors
0.36
2
1
Name
Order
Citations
PageRank
Matt Crane111811.17