METEOR

METEOR (Metric for Evaluation of Translation with Explicit ORdering) is a metric for the evaluation of machine translation output. The metric is based on the harmonic mean of unigram precision and recall, with recall weighted higher than precision. It also has several features that are not found in other metrics, such as stemming and synonymy matching, along with the standard exact word matching. The metric was designed to fix some of the problems found in the more popular BLEU metric, and also produce good correlation with human judgement at the sentence or segment level This differs from the BLEU metric in that BLEU seeks correlation at the corpus level.Results have been presented which give correlation of up to 0.964 with human judgement at the corpus level, compared to BLEU's achievement of 0.817 on the same data set. At the sentence level, the maximum correlation with human judgement achieved was 0.403.ref|Banerjee2005

Algorithm

As with BLEU, the basic unit of evaluation is the sentence, the algorithm first creates an "alignment" (see illustrations) between two sentences, the candidate translation string, and the reference translation string. The "alignment" is a set of "mappings" between unigrams. A mapping can be thought of as a line between a unigram in one string, and a unigram in another string. The constraints are as follows; every unigram in the candidate translation must map to zero or one unigram in the reference translation and vice versa. In any alignment, a unigram in one string cannot map to more than one unigram in another string.

An alignment is created incrementally through a series of "stages", which are controlled by "modules". A module is simply a matching algorithm, for example the "wn_synonymy" module maps synonyms using WordNet, while the "exact" module matches exact words. Examples are given as follows:

Each stage is split up into two "phases". In the first phase, all possible unigram mappings are collected for the module being used in this stage. In the second phase, the largest subset of these mappings is selected to produce an "alignment" as defined above. If there are two alignments with the same number of mappings, the alignment is chosen with the fewest "crosses", that is, with fewer intersections of two mappings. From the two alignments shown, alignment (a) would be selected at this point. Stages are run consecutively and each stage only adds to the alignment those unigrams which have not been matched in previous stages. Once the final alignment is computed, the score is computed as follows: Unigram precision $P$ is calculated as:

Score: 0.9977 = Fmean: 1.0000 * (1 - Penalty: 0.0023)Fmean: 1.0000 = 10 * Precision: 1.0000 * Recall: 1.0000 / Recall: 1.0000 + 9 * Precision: 1.0000Penalty: 0.0023 = 0.5 * (Fragmentation: 0.1667 ^3) Fragmentation: 0.1667 = Chunks: 1.0000 / Matches: 6.0000

Score: 0.9654 = Fmean: 0.9836 * (1 - Penalty: 0.0185)Fmean: 0.9836 = 10 * Precision: 0.8571 * Recall: 1.0000 / Recall: 1.0000 + 9 * Precision: 0.8571Penalty: 0.0185 = 0.5 * (Fragmentation: 0.3333 ^3)Fragmentation: 0.3333 = Chunks: 2.0000 / Matches: 6.0000

Notes

# Banerjee, S. and Lavie, A. (2005)

References

* Banerjee, S. and Lavie, A. (2005) "METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments" in "Proceedings of Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization at the 43rd Annual Meeting of the Association of Computational Linguistics (ACL-2005), Ann Arbor, Michigan, June 2005"
* Lavie, A., Sagae, K. and Jayaraman, S. (2004) "The Significance of Recall in Automatic Metrics for MT Evaluation" in "Proceedings of AMTA 2004, Washington DC. September 2004"

External links

* [http://www.cs.cmu.edu/~alavie/METEOR/ The METEOR Automatic Machine Translation Evaluation System] (including link for download)