Deep Learning for Natural Language Processing

About

This repository contains code for mini-project of MSc. course Deep Learning for Natural Language Processing.

Description

We cover the classical NLP problem of question classification, which consists of two parts.

Classification Task

• We choose Facebook's FastText as our baseline, and further implement two neural models, namely LSTM and TextCNN.

• The three models are compared in terms of the overall classification accuracy, and the precision, recall and F1-score values for each category.

Rationale Extraction

• A layer of binary latent variables is added to our neural models that select what parts of the input expose features for classification. This is used for better interpretability of our models.

Dataset

The dataset we use can be found here.

Prerequisites

1. Install the conda environment by running conda env create -f environment.yml.

2. Then activate it by conda activate dlnlp.

3. [Optional] Download the pre-trained Word2Vec word embeddings and unzip into folder preprocessing.

Running Instructions

• [Optional] Preprocess the dataset and extract word embeddings by running python -m dataset.

• Test the FastText model by running python -m FastText --mode=eval.

• Test the LSTM model by running python -m LSTM --mode=eval.

• Test the TextCNN model by running python -m TextCNN --mode=eval.

• Test the Rationale extraction model by running python -m Rationale --mode=eval.