#Natural Language Generation

This repository offers code to evaluate unsupervised metrics in Natural Language Generation. It processes hypothesis and reference inputs to compute various metrics like BLEU, ROUGE, and CIDEr. Java and Python dependencies are needed for installation, and it can be used via command line or Python API. It supports assessments of both individual and multiple examples, providing flexibility for research applications. Users can customize settings for metrics like CIDEr to enhance precision.

Discover a detailed repository on dialogue systems and natural language generation in Conversational AI. This continually updated resource lists significant papers emphasizing deep learning in Natural Language Processing (NLP). Covering diverse topics, including dialogue evaluation, multimodal dialogues, and proactive as well as open-domain conversations, it provides key insights into contemporary research trends and methods, aiding researchers and practitioners in expanding their understanding of advancements in Conversational AI.