Which metrics are used in machine translation?

Which metrics are used in machine translation?

(2005) highlight five attributes that a good automatic metric must possess; correlation, sensitivity, consistency, reliability and generality. Any good metric must correlate highly with human judgment, it must be consistent, giving similar results to the same MT system on similar text.

What is fluency in machine translation?

Fluency measures whether a translation is fluent, regard- less of the correct meaning, while Adequacy mea- sures whether the translation conveys the correct meaning, even if the translation is not fully flu- ent.

How do you evaluate a translation?

Evaluating a Translation

1. First and foremost, a translation should be accurate.
2. Secondly, a translation should be grammatically correct and contain no syntax or spelling errors.
3. Thirdly, a translation should of course be complete.
4. Furthermore, a translation should be consistent.

How do you calculate Bleu?

in their 2002 paper “BLEU: a Method for Automatic Evaluation of Machine Translation“. The approach works by counting matching n-grams in the candidate translation to n-grams in the reference text, where 1-gram or unigram would be each token and a bigram comparison would be each word pair.

What is the error rate of protein synthesis?

During protein synthesis, mistakes are generally thought to occur at a rate of about 1 in every 20,000 amino acids, although levels can be higher or lower depending on the conditions2,3.

How often are mistakes made in DNA translation?

Nonetheless, these enzymes do make mistakes at a rate of about 1 per every 100,000 nucleotides. That might not seem like much, until you consider how much DNA a cell has. In humans, with our 6 billion base pairs in each diploid cell, that would amount to about 120,000 mistakes every time a cell divides!

What makes a good metric for machine translation?

Any good metric must correlate highly with human judgment, it must be consistent, giving similar results to the same MT system on similar text. It must be sensitive to differences between MT systems and reliable in that MT systems that score similarly should be expected to perform similarly.

How is the output of a machine translation system evaluated?

A number of methods for the evaluation of the output from these systems were tested in 1992 and the most recent suitable methods were selected for inclusion in the programmes for subsequent years. The methods were; comprehension evaluation, quality panel evaluation, and evaluation based on adequacy and fluency.

Which is more reliable machine translation or human translation?

The evaluation methodology managed to separate translations by humans from translations by machines with ease. The study concluded that, “highly reliable assessments can be made of the quality of human and machine translations”.

How are machine translation systems evaluated in ARPA?

Measuring systems based on adequacy and fluency, along with informativeness is now the standard methodology for the ARPA evaluation program. In the context of this article, a metric is a measurement. A metric that evaluates machine translation output represents the quality of the output.