@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/ingest-emnlp/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."
}