BERTScore: Evaluating Text Generation with BERT

Presented by

Gursimran Singh

Introduction

In recent times, various machine learning approaches for text generation have gained popularity. The idea behind this paper is to develop an automatic metric that will judge the quality of the generated text. Commonly used state of the art metrics either uses n-gram approach or word embeddings for calculating the similarity between the reference and the candidate sentence. BertScore, on the other hand, calculates the similarity using contextual embeddings. The authors of the paper have carried out various experiments in Machine Translation and Image Captioning to show why BertScore is more reliable and robust than the previous approaches.