Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Task

Training huge unsupervised deep neural networks yields to strong progress in the field of Natural Language Processing (NLP). Using these extensively pre-trained networks for particular NLP applications is the current state-of-the-art approach. In this project, we approach the task of ranking possible clarifying questions for a given query. We fine-tuned a pre-trained BERT model to rank the possible clarifying questions in a classification manner. The achieved model scores a top-5 accuracy of 0.4565 on the provided benchmark dataset.

Installation

This project was originally developed with Python 3.8, PyTorch 1.7, and CUDA 11.0. The training requires one NVIDIA GeForce RTX 1080 (11GB memory).

• Create conda environment: