Phrase-based translation
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Southern California
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 May 2003
- Authors
- Philipp Koehn, Franz Josef Och, Daniel Marcu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 9.2M
- Training data
- 20,000,000 tokens
There are various components to the system: - Translation probability model phi - The distortion probability distribution d - A langage model p_LM - A length factor w Several translation probability models are considered. The most performant one is the AP word alignment model. The sentence length preferred by the authors is 3 words maximum. In the biggest corpus considered (320k phrase pairs) it produces a phrase translation probability table of 1996k entries. The distortion probability model…
[WORDS] "We used the freely available Europarl corpus to carry out experiments. This corpus contains over 20 million words in each of the eleven official languages of the European Union, covering the proceedings of the European Parliament 1996-2001. 1755 sentences of length 5-15 were reserved for testing." "These results are consistent over training corpus sizes from 10,000 sentence pairs to 320,000 sentence pairs. " So 20 million words or 320k sentence pairs.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 4,270
Sources
Where this record came from and when it was last checked.
- Reference
- Statistical Phrase-Based Translation
- Last updated
- 28 November 2025
What the numbers mean
Background
Phrase-based translation was published by University of Southern California, in United States of America, in May 2003. academia is the category the publisher falls under.
It works in Language, and is recorded as doing translation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Around 20,000,000 tokens went into training it.
Answers
Phrase-based translation — common questions
When was Phrase-based translation released?
Phrase-based translation was published in May 2003. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Phrase-based translation used for?
Phrase-based translation works in Language, and is recorded as handling translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Phrase-based translation?
None. Phrase-based translation is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Phrase-based translation open source?
The licensing for Phrase-based translation was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Phrase-based translation have?
Phrase-based translation has 9.2M parameters. There are various components to the system: - Translation probability model phi - The distortion probability distribution d - A langage model p_LM - A length factor w Several translation probability models are considered. The most performant one is the AP word alignment model. The sentence length preferred by the authors is 3 words maximum. In the biggest corpus considered (320k phrase pairs) it produces a phrase translation probability table of 1996k entries. The distortion probability model d is taken from (Marcu and Wong, 2002). The distortion probability model must have ~10 parameters at most The language model p_LM is a back off trigram model from (Seymore and Rosenfeld,1997). AFAIK the cutoff used is not specified. Based on the example on section 4.3 of (Seymore and Rosefeld, 1997), a trigram probability model has about 3866964 + 2674322 + 641604 parameters. "For each possible phrase translation anywhere in the sentence (we call it a translation option), we multiply its phrase translation probability with the language model probability for the generated English phrase. As language model probability we use the unigram probability for the first word, the bigram probability for the second, and the trigram probability for all following words" The length factor w is an additional single parameter. "In order to calibrate the output length, we introduce a factor w for each generated English word in addition to the trigram language model " In summary, the parameter count seems to be dominated by the trigram language model and the word alignment phrase translation model. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Phrase-based translation?
Phrase-based translation was published by University of Southern California, based in United States of America, categorised as academia.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.