A strong baseline for question relevancy ranking
Computation and Language
2018-08-28 v1 Information Retrieval
Machine Learning
Abstract
The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks -- a task that amounts to question relevancy ranking -- involve complex pipelines and manual feature engineering. Despite this, many of these still fail at beating the IR baseline, i.e., the rankings provided by Google's search engine. We present a strong baseline for question relevancy ranking by training a simple multi-task feed forward network on a bag of 14 distance measures for the input question pair. This baseline model, which is fast to train and uses only language-independent features, outperforms the best shared task systems on the task of retrieving relevant previously asked questions.
Cite
@article{arxiv.1808.08836,
title = {A strong baseline for question relevancy ranking},
author = {Ana V. González-Garduño and Isabelle Augenstein and Anders Søgaard},
journal= {arXiv preprint arXiv:1808.08836},
year = {2018}
}
Comments
To appear at EMNLP 2018