KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering

Simone Filice, Giovanni Da San Martino, Alessandro Moschitti


Abstract
This paper describes the KeLP system participating in the SemEval-2017 community Question Answering (cQA) task. The system is a refinement of the kernel-based sentence pair modeling we proposed for the previous year challenge. It is implemented within the Kernel-based Learning Platform called KeLP, from which we inherit the team’s name. Our primary submission ranked first in subtask A, and third in subtasks B and C, being the only systems appearing in the top-3 ranking for all the English subtasks. This shows that the proposed framework, which has minor variations among the three subtasks, is extremely flexible and effective in tackling learning tasks defined on sentence pairs.
Anthology ID:
S17-2053
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:
326–333
Language:
URL:
https://www.aclweb.org/anthology/S17-2053
DOI:
10.18653/v1/S17-2053
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2053.pdf