Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection

Byron Galbraith, Bhanu Pratap, Daniel Shank


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.
Anthology ID:
S17-2062
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
375–379
Language:
URL:
https://www.aclweb.org/anthology/S17-2062
DOI:
10.18653/v1/S17-2062
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2062.pdf