Adaptive Convolutional Filter Generation for Natural Language Understanding.
Year: 2017
Type: preprint
Abstract: Convolutional neural networks (CNNs) have recently emerged as a popularbuilding block for natural language processing (NLP). Despite their success,
most existing CNN models employed in NLP are not expressive enough, in the
sense that all input sentences share the same learned (and static) set of
filters. Motivated by this problem, we propose an adaptive convolutional filter
generation framework for natural language understanding, by leveraging a meta
network to generate input-aware filters. We further generalize our framework to
model question-answer sentence pairs and propose an adaptive question answering
(AdaQA) model; a novel two-way feature abstraction mechanism is introduced to
encapsulate co-dependent sentence representations. We investigate the
effectiveness of our framework on document categorization and answer
sentence-selection tasks, achieving state-of-the-art performance on several
benchmark datasets.
Source: arXiv (Cornell University)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2757937538