This repository contains the data and scripts needed to build the website for the Brandeis CS/CL Ph.D. seminars. The site is built from a data file, data.json, and a folder of PDFs, papers.
The data.json file contains simple entries for each seminar:
{ "title": "Neural Discourse Parsing",
"speaker": "Te Rutherford",
"url": "http://www.cs.brandeis.edu/~tet/",
"date": "March 23rd, 2016",
"abstract": "Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Many neural network models have been proposed to tackle this problem. However, the comparison for this task is not unified, so we could hardly draw clear conclusions about the effectiveness of various architectures. Here, we propose neural network models that are based on feedforward and long-short term memory architecture and systematically study the effects of varying structures. To our surprise, the best-configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Further, we compare our best feedforward system with competitive convolutional and recurrent networks and find that feedforward can actually be more effective. For the first time for this task, we compile and publish outputs from previous neural and non-neural systems to establish the standard for further comparison.",
"paper": "neural_discourse_final.pdf"
}The paper field refers to the name of a PDF in the papers folder. To build the website:
# install needed dependencies
pipenv shell
pipenv install
# run the script
makeThe website will be rendered and placed in the output directory. The make deploy option automatically SCPs the files to the Brandeis webserver.