There are several common evaluation metrics for machine translation (MT), which are used to assess the quality and performance of MT systems. Some of the most widely used metrics are:

  1. BLEU (Bilingual Evaluation Understudy): BLEU is a popular metric that measures the similarity between the output of an MT system and one or more human-generated reference translations. It calculates the precision of the system's output compared to the references and penalizes for brevity. BLEU scores range from 0 to 1, with higher scores indicating better translation quality.

  2. METEOR (Metric for Evaluation of Translation with Explicit ORdering): METEOR is another widely used metric that combines precision, recall, and alignment-based measures to evaluate MT output. It takes into account multiple reference translations and performs explicit word order matching. METEOR scores range from 0 to 1, with higher scores indicating better translation quality.

  3. TER (Translation Edit Rate): TER measures the number of edits required to transform the MT output into one or more human-generated reference translations. It calculates the edit operations (insertion, deletion, substitution) needed to align the output with the references. Lower TER scores indicate better translation quality.

  4. NIST (Normalized Information Retrieval Score): NIST evaluates the output of an MT system based on the performance of an information retrieval system. It measures the similarity between the output and multiple references using n-gram matching. Higher NIST scores indicate better translation quality.

  5. HTER (Human-targeted translation Error Rate): HTER is a metric that measures the similarity between the MT output and a human-generated reference translation based on edit distance. It represents the percentage of edits required to transform the MT output into the reference. Lower HTER scores indicate better translation quality.

  These metrics provide different perspectives on translation quality, and their choice depends on the specific evaluation requirements and preferences of the researchers or practitioners. It is common to use multiple metrics together to obtain a more comprehensive evaluation of an MT system's performance.