What is the METEOR Score (Metric for Evaluation of Translation with Explicit Ordering)?

What is the METEOR Score (Metric for Evaluation of Translation with Explicit Ordering)?

The METEOR score is a metric used to evaluate machine translation by comparing it to human translations. It takes into account both the accuracy and fluency of the translation, as well as the order in which words appear. The METEOR score ranges from 0 to 1, with a higher score indicating better translation quality.

The algorithm behind the METEOR score compares the translated text to the human reference translation by breaking them down into chunks and calculating the similarity between each chunk using various measures such as unigram precision, recall, and F-score, bigram overlap, and exact word matches. Finally, the weighted average of these measures is used to calculate the overall METEOR score.

What are its key features?

The key features of the METEOR score include:

1. It evaluates both the accuracy and fluency of translations.
2. It takes into account the order in which words appear in the translation, unlike other metrics like BLEU that do not consider word order.
3. It uses a weighted average of various measures to calculate the overall score, allowing for greater flexibility in adjusting the weights based on different criteria.
4. It can handle multiple reference translations, making it suitable for evaluating translations with more than one possible correct answer.

How does it work?

The METEOR score works by breaking down both the machine translation and the human reference translation into chunks of text, typically words or phrases.

It then compares each chunk from the machine translation to every chunk in the human reference translation using various measures such as unigram precision, recall, and F-score, bigram overlap, and exact word matches. The weighted average of these measures is used to calculate the overall METEOR score, with each measure being given a different weight based on its importance in evaluating translation quality.

Finally, the algorithm combines all the scores from each chunk to produce an overall METEOR score for the entire translation.

What are its benefits?

The benefits of using the METEOR score include:

1. It provides a more comprehensive evaluation of translation quality than other metrics like BLEU, which only consider word accuracy and do not take into account fluency or word order.
2. It is suitable for evaluating translations with multiple reference translations, making it useful for tasks where there may be more than one correct answer.
3. It allows for greater flexibility in adjusting the weights of different measures based on specific criteria or needs.
4. It has been shown to have a high correlation with human judgments of translation quality, indicating that it is an effective metric for evaluating machine translations.

What are its limitations?

Some limitations of the METEOR score include:

1. It may not be as sensitive to small differences in translation accuracy as other metrics like BLEU, which can make it less useful for evaluating minor improvements or errors.
2. It requires a human reference translation, which may not always be available or feasible to obtain.
3. The algorithm behind the METEOR score is relatively complex and computationally intensive, which can make it slower and more resource-intensive than other metrics.
4. The weights assigned to different measures in the calculation of the METEOR score may not always be optimal or representative of the desired criteria for evaluating translation quality.