| Pre-trained evaluator in EMNLP 2022 paper | |
| *[Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://arxiv.org/abs/2210.07197)* | |
| ## Introduction | |
| **Multi-dimensional evaluation** is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency. | |
| However, automatic evaluation in NLG is still dominated by similarity-based metrics (e.g., ROUGE, BLEU), but they are not sufficient to portray the difference between the advanced generation models. | |
| Therefore, we propose **UniEval** to bridge this gap so that a more comprehensive and fine-grained evaluation of NLG systems can be achieved. | |
| ## Pre-trained Evaluator | |
| **unieval-sum** is the pre-trained evaluator for the text summarization task. It can evaluate the model output from four dimensions: | |
| - *coherence* | |
| - *consistency* | |
| - *fluency* | |
| - *relevance* | |
| It can also be transferred to the new dimensions and generation tasks, such as *naturalness* and *informativeness* for data-to-text. | |
| ## Usage | |
| Please refer to [our GitHub repository](https://github.com/maszhongming/UniEval). |