Simple Convolutional Neural Networks with Linguistically-Annotated Input for Answer Selection in Question Answering
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Date
2018-08-10
Authors
Sequiera, Royal
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
With the advent of deep learning methods, researchers have been increasingly preferring
deep learning methods over decades-old feature-engineering-inspired work in Natural Language Processing (NLP). The research community has been moving away from otherwise dominant feature engineering approaches; rather, is gravitating towards more complicated
neural architectures. Highly competitive tools like part-of-speech taggers that exhibit
human-like accuracy are traded off for complex networks, with the hope that the neural
network will learn the features needed. In fact, there have been efforts to do NLP "from
scratch" with neural networks that altogether eschew featuring engineering based tools
(Collobert et al, 2011). In our research, we modify the input that is fed to neural networks
by annotating the input with linguistic information: POS tags, Named Entity Recognition
output, linguistic relations, etc. With just the addition of these linguistic features on a
simple Siamese convolutional neural network, we are able to achieve state-of-the-art results.
We argue that this strikes a better balance between feature vs. network engineering.
Description
Keywords
Natural Language Processing, NLP, Question Answering, Answer selection, CNN, Feature engineering, neural networks