**EMQAP**, or **E-Manual Question Answering Pipeline**, is an approach for answering questions pertaining to electronics devices. Built upon the pretrained [RoBERTa](https://paperswithcode.com/method/roberta), it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section.

A **Global Convolutional Network**, or **GCN**, is a semantic segmentation building block that utilizes a large kernel to help perform classification and localization tasks simultaneously. It can be used in a [FCN](https://paperswithcode.com/method/fcn)-like structure, where the [GCN](https://paperswithcode.com/method/gcn) is used to generate semantic score maps. Instead of directly using larger kernels or global [convolution](https://paperswithcode.com/method/convolution), the GCN module employs a combination of $1 \times k + k \times 1$ and $k \times 1 + 1 \times k$ convolutions, which enables [dense connections](https://paperswithcode.com/method/dense-connections) within a large
$k\times{k}$ region in the feature map

Global Convolutional Network

Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network

EMQAP

Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework

A generic way of representing and interpolating labels, which allows straightforward extension of any kind of [mixup](https://paperswithcode.com/method/mixup) to deep metric learning for a large class of loss functions.

Source	Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com