If You Can’t Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking

Dasha Bogdanova, Jennifer Foster, Daria Dzendzik, Qun Liu


Abstract
We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features. We present a neural network architecture based on a combination of recurrent neural networks that are used to encode questions and answers, and a multilayer perceptron. We show how this approach can be combined with additional features, in particular, the discourse features used by previous research. Our neural approach achieves state-of-the-art performance on a public dataset from Yahoo! Answers and its performance is further improved by incorporating the discourse features. Additionally, we present a new dataset of Ask Ubuntu questions where the hybrid approach also achieves good results.
Anthology ID:
E17-1012
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–131
Language:
URL:
https://www.aclweb.org/anthology/E17-1012
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1012.pdf