@inproceedings{gonzalez-etal-2018-strong,
title = "A strong baseline for question relevancy ranking",
author = "Gonzalez, Ana and
Augenstein, Isabelle and
S{\o}gaard, Anders",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D18-1515/",
doi = "10.18653/v1/D18-1515",
pages = "4810--4815",
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."
}
Markdown (Informal)
[A strong baseline for question relevancy ranking](https://preview.aclanthology.org/jlcl-multiple-ingestion/D18-1515/) (Gonzalez et al., EMNLP 2018)
ACL
- Ana Gonzalez, Isabelle Augenstein, and Anders Søgaard. 2018. A strong baseline for question relevancy ranking. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4810–4815, Brussels, Belgium. Association for Computational Linguistics.