1 code implementation • 7 Sep 2021 • Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander M. Rush, Thomas Wolf
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks.
In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora.
We propose a methodology to construct a hybrid model that leverages (a) Convolutional Neural Network (CNN) to deal with spatial information in an image, and (b) Graph Neural Network (GNN) to deal with relational superpixel information in the image.
In our paper, we explore simple versions of both of these approaches and their performance on the task.
Given a fill-in-the-blank-type question and a corresponding context, the task is to predict the most suitable word from a list of 5 options.