Compositional Attention Networks for Interpretability in Natural Language Question Answering
Abstract
MAC Net is a compositional attention network designed for Visual Question Answering. We propose a modified MAC net architecture for Natural Language Question Answering. Question Answering typically requires Language Understanding and multi-step Reasoning. MAC net's unique architecture - the separation between memory and control, facilitates data-driven iterative reasoning. This makes it an ideal candidate for solving tasks that involve logical reasoning. Our experiments with 20 bAbI tasks demonstrate the value of MAC net as a data-efficient and interpretable architecture for Natural Language Question Answering. The transparent nature of MAC net provides a highly granular view of the reasoning steps taken by the network in answering a query.
Cite
@article{arxiv.1810.12698,
title = {Compositional Attention Networks for Interpretability in Natural Language Question Answering},
author = {Muru Selvakumar and Suriyadeepan Ramamoorthy and Vaidheeswaran Archana and Malaikannan Sankarasubbu},
journal= {arXiv preprint arXiv:1810.12698},
year = {2018}
}
Comments
8 pages,10 figures, 1 table