Paper Info
Title
An Experimental Study of Neural Approaches to Multi-Hop Inference in Question Answering
Abstract
Question answering aims at computing the answer to a question given a context with facts. Many proposals focus on questions whose answer is explicit in the context; lately, there has been an increasing interest in questions whose answer is not explicit and requires multi-hop inference to be computed. Our analysis of the literature reveals that there is a seminal proposal with increasingly complex follow-ups. Unfortunately, they were presented without an extensive study of their hyper-parameters, the experimental studies focused exclusively on English, and no statistical analysis to sustain the conclusions was ever performed. In this paper, we report on our experience devising a very simple neural approach to address the problem, on our extensive grid search over the space of hyper-parameters, on the results attained with English, Spanish, Hindi, and Portuguese, and sustain our conclusions with statistically sound analyses. Our findings prove that it is possible to beat many of the proposals in the literature with a very simple approach that was likely overlooked due to the difficulty to perform an extensive grid search, that the language does not have a statistically significant impact on the results, and that the empirical differences found among some existing proposals are not statistically significant.
Year
DOI
Venue
2022
10.1142/S0129065722500113
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
DocType
Volume
Natural language processing, question answering, multi-hop inference, neural networks
Journal
32
Issue
ISSN
Citations 
04
0129-0657
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Patricia Jiménez101.01
Rafael Corchuelo238949.87