# Copyright 2020 The HuggingFace Evaluate Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ METEOR metric. """ from dataclasses import dataclass import datasets import numpy as np from datasets.config import importlib_metadata, version from nltk.translate import meteor_score import evaluate NLTK_VERSION = version.parse(importlib_metadata.version("nltk")) if NLTK_VERSION >= version.Version("3.6.4"): from nltk import word_tokenize _CITATION = """\ @inproceedings{banarjee2005, title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments}, author = {Banerjee, Satanjeev and Lavie, Alon}, booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization}, month = jun, year = {2005}, address = {Ann Arbor, Michigan}, publisher = {Association for Computational Linguistics}, url = {https://www.aclweb.org/anthology/W05-0909}, pages = {65--72}, } """ _DESCRIPTION = """\ METEOR, an automatic metric for machine translation evaluation that is based on a generalized concept of unigram matching between the machine-produced translation and human-produced reference translations. Unigrams can be matched based on their surface forms, stemmed forms, and meanings; furthermore, METEOR can be easily extended to include more advanced matching strategies. Once all generalized unigram matches between the two strings have been found, METEOR computes a score for this matching using a combination of unigram-precision, unigram-recall, and a measure of fragmentation that is designed to directly capture how well-ordered the matched words in the machine translation are in relation to the reference. METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic data and 0.331 on the Chinese data. This is shown to be an improvement on using simply unigram-precision, unigram-recall and their harmonic F1 combination. """ _KWARGS_DESCRIPTION = """ Computes METEOR score of translated segments against one or more references. Args: predictions: list of predictions to score. Each prediction should be a string with tokens separated by spaces. references: list of reference for each prediction. Each reference should be a string with tokens separated by spaces. alpha: Parameter for controlling relative weights of precision and recall. default: 0.9 beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3 gamma: Relative weight assigned to fragmentation penalty. default: 0.5 Returns: 'meteor': meteor score. Examples: >>> meteor = evaluate.load('meteor') >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"] >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"] >>> results = meteor.compute(predictions=predictions, references=references) >>> print(round(results["meteor"], 4)) 0.6944 """ @dataclass class MeteorConfig(evaluate.info.Config): name: str = "default" alpha: float = 0.9 beta: float = 3.0 gamma: float = 0.5 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) class Meteor(evaluate.Metric): CONFIG_CLASS = MeteorConfig ALLOWED_CONFIG_NAMES = ["default", "multilabel"] def _info(self, config): return evaluate.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, config=config, features=[ datasets.Features( { "predictions": datasets.Value("string", id="sequence"), "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"), } ), datasets.Features( { "predictions": datasets.Value("string", id="sequence"), "references": datasets.Value("string", id="sequence"), } ), ], codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"], reference_urls=[ "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score", "https://en.wikipedia.org/wiki/METEOR", ], ) def _download_and_prepare(self, dl_manager): import nltk nltk.download("wordnet") if NLTK_VERSION >= version.Version("3.6.5"): nltk.download("punkt") if NLTK_VERSION >= version.Version("3.6.6"): nltk.download("omw-1.4") def _compute(self, predictions, references): multiple_refs = isinstance(references[0], list) if NLTK_VERSION >= version.Version("3.6.5"): # the version of METEOR in NLTK version 3.6.5 and earlier expect tokenized inputs if multiple_refs: scores = [ meteor_score.meteor_score( [word_tokenize(ref) for ref in refs], word_tokenize(pred), alpha=self.config.alpha, beta=self.config.beta, gamma=self.config.gamma, ) for refs, pred in zip(references, predictions) ] else: scores = [ meteor_score.single_meteor_score( word_tokenize(ref), word_tokenize(pred), alpha=self.config.alpha, beta=self.config.beta, gamma=self.config.gamma, ) for ref, pred in zip(references, predictions) ] else: if multiple_refs: scores = [ meteor_score.meteor_score( [[word_tokenize(ref) for ref in group] for group in references][0], word_tokenize(pred), alpha=self.config.alpha, beta=self.config.beta, gamma=self.config.gamma, ) for ref, pred in zip(references, predictions) ] else: scores = [ meteor_score.single_meteor_score( ref, pred, alpha=self.config.alpha, beta=self.config.beta, gamma=self.config.gamma ) for ref, pred in zip(references, predictions) ] return {"meteor": np.mean(scores)}