From caf9e137fa0469d235d8db6c49eb64d037db070b Mon Sep 17 00:00:00 2001 From: joergfranke Date: Tue, 10 Jul 2018 16:12:50 +0200 Subject: [PATCH] update README with links --- README.md | 28 ++++++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9fd770b..ff103da 100644 --- a/README.md +++ b/README.md @@ -7,15 +7,31 @@ *This repository is under construction and some comments and functions are missing.* This repository contains a implementation of a Differentiable Neural Computer (DNC) with advancements for a more robust and -scalable usage in Question Answering. It is applied to: +scalable usage in Question Answering. It is published on the MRQA workshop at the ACL 2018. + +- MRQA 2018 paper submission [Robust and Scalable Differentiable Neural Computer for Question Answering](https://arxiv.org/abs/1807.02658) +- More detailed master thesis about the [Advanced DNC for Question Answering](http://isl.anthropomatik.kit.edu/cmu-kit/downloads/Master_Franke_2018.pdf) + +The ADNC is applied to: - [20 bAbI QA tasks](https://research.fb.com/downloads/babi/) with [state-of-the-art results](#babi-results) -- [CNN Reading Comprehension Task](https://github.com/danqi/rc-cnn-dailymail) with -[passable results](#cnn-results) without any adaptation or hyper-parameter tuning. +- [CNN Reading Comprehension Task](https://github.com/danqi/rc-cnn-dailymail) with +[passable results](#cnn-results) without any adaptation or hyper-parameter tuning. -This repository is the groundwork for the MRQA 2018 -paper submission "Robust and Scalable Differentiable Neural Computer for Question Answering". It contains a modular and -fully configurable DNC with the following advancements: +The implementation provides the following features: +- Multi-read and multi-write head memory units +- Key implementations (memory unit, controller, etc. ) have tests +- Modular implementation of memory unit and controller +- Fully configurable by a yaml-file +- Pre-trained models on bAbI task and CNN RC task +- Plot of the memory unit functionality during inference +- The following advancements are implemented: + + +