Description
Overview
Privacy-preservation is an important issue in machine learning. In quite a few application scenarios (e.g., healthcare, banking, legal fields, private message processing), real data may not be shown to the developer of a data-driven machine learning model (for NLU or other tasks).
In this assignment, you will experiment with a simple scenario for privacy-preserving NLU: developping a text classification model based on ciphertext. You will be provided with a training set and a development set of (binary) labeled ciphertext, as well as an unlabeled test set. All the sentences in the dataset are encrypted on the word level (hence, words in a sentence are still separated by white spaces).
You are free to experiment with any methods of representation, encoding and classifying the ciphertext using the training and dev data, and submit your prediction on the unlabeled test set. We will compare your prediction with the ground-truth labels of the test set on our side. Grading will be based on the ranking of your submitted prediction among all of those in the class.
(Please make sure to read the Q&A part)
Data and Jupyter Notebook
A compressed ZIP file is to be released on Blackboard when the assignment is available. The uncompressed archive contain the following files:
main.ipynb: The Python 3 Jupyter notebook you will need to fill in with your training and inference code, and predict the results.
Dataset:
train_enc.tsv: labeled training data. Each line a ‘\t’ separated tuple in the form of (label, ciphertext sentence).
dev_enc.tsv: labeled dev data.
test_enc_unlabeled.tsv: unlabled test data. Every line is a ciphertext sentence.
upload_document.txt: the documentation file where you need to fill in the blanks to describe your method.
Programs
The provided Jupyter notebook already contains the beginning part of code for reading the data, and the end part to generate “upload_predictions.txt”. You need to fill in the “Main Code Body”, and put the 2028 predictioned labels into the results list.
Restrictions. Your method needs to be implemented using python 3, and should be executable on a single personal machine (e.g., your own laptop). Other than those, you are free to use any python package (e.g., pytorch, gensim, sklearn, fasttext, etc.).
Submission
This assignment requests submitting three files (DO NOT CHANGE the filenames!):
upload_predictions.txt: The predictions of your model on the 2028 unlabeled test set ciphertext sentences. This file should have exactly 2028 lines, every line is either 0 or 1. (submit this on Vocareum)
upload_document.txt: Fill in the blanks of that file to accordingly describe how your model represents, encodes and classifies the ciphertext, and how you have developed the model. (submit this on Blackboard)
main.ipynb: This Jupyter notebook already contains the beginning part to read the data, and the end part to generate “upload_predictions.txt”. You need to fill in the “Main Code Body” (submit this on Blackboard).
Multiple submissions are allowed; only the final submission will be graded. Do not include any other files in your submission. You are encouraged to submit early and often in order to iron out any problems, especially issues with the format of the final output.
We will grade using a leaderboard protocol: the ground-truth labels on the test set are not released to you, and we will compare your prediction with them to calculate the accuracy of your prediction.
The prediction of your predictions will be measured automatically; failure to format your output correctly may result in very low scores, which will not be changed.
For full credit, make sure to submit your assignment well before the deadline. The time of submission recorded by the system is the time used for determining late penalties. If your submission is received late, whatever the reason (including equipment failure and network latencies or outages), it will incur a late penalty.
