Metric: sari

#### Description

SARI is a metric used for evaluating automatic text simplification systems. The metric compares the predicted simplified sentences against the reference and the source sentences. It explicitly measures the goodness of words that are added, deleted and kept by the system. Sari = (F1_add + F1_keep + P_del) / 3 where F1_add: n-gram F1 score for add operation F1_keep: n-gram F1 score for keep operation P_del: n-gram precision score for delete operation n = 4, as in the original paper. This implementation is adapted from Tensorflow's tensor2tensor implementation [3]. It has two differences with the original GitHub [1] implementation: (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match a target exactly. (2) Fixes an alleged bug [2] in the keep score computation. [1] https://github.com/cocoxu/simplification/blob/master/SARI.py (commit 0210f15) [2] https://github.com/cocoxu/simplification/issues/6 [3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py

How to load this metric directly with the datasets library:

from datasets import load_metric
metric = load_metric("sari")

#### Citation

@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}