@inproceedings{galbraith-etal-2017-talla,
    title = "Talla at {S}em{E}val-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection",
    author = "Galbraith, Byron  and
      Pratap, Bhanu  and
      Shank, Daniel",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S17-2062/",
    doi = "10.18653/v1/S17-2062",
    pages = "375--379",
    abstract = "This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7{\%} out of max achievable 67.0{\%} on the test set."
}