Translation Edit Rate (TER)¶

Module Interface¶

class torchmetrics.TranslationEditRate(normalize=False, no_punctuation=False, lowercase=True, asian_support=False, return_sentence_level_score=False, **kwargs)[source]

Calculate Translation edit rate (TER) of machine translated text with one or more references.

This implementation follows the one from SacreBleu_ter, which is a near-exact reimplementation of the Tercom algorithm, produces identical results on all “sane” outputs.

As input to forward and update the metric accepts the following input:

preds (Sequence): An iterable of hypothesis corpus
target (Sequence): An iterable of iterables of reference corpus

As output of forward and compute the metric returns the following output:

ter (Tensor): if return_sentence_level_score=True return a corpus-level translation edit rate with a list of sentence-level translation_edit_rate, else return a corpus-level translation edit rate

Parameters

normalize¶ (bool) – An indication whether a general tokenization to be applied.
no_punctuation¶ (bool) – An indication whteher a punctuation to be removed from the sentences.
lowercase¶ (bool) – An indication whether to enable case-insesitivity.
asian_support¶ (bool) – An indication whether asian characters to be processed.
return_sentence_level_score¶ (bool) – An indication whether a sentence-level TER to be returned.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> ter = TranslationEditRate()
>>> ter(preds, target)
tensor(0.1538)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface¶

torchmetrics.functional.translation_edit_rate(preds, target, normalize=False, no_punctuation=False, lowercase=True, asian_support=False, return_sentence_level_score=False)[source]

Calculate Translation edit rate (TER) of machine translated text with one or more references. This implementation follows the implmenetaions from https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/metrics/ter.py. The sacrebleu implmenetation is a near-exact reimplementation of the Tercom algorithm, produces identical results on all “sane” outputs.

Parameters

preds¶ (Union[str, Sequence[str]]) – An iterable of hypothesis corpus.
target¶ (Sequence[Union[str, Sequence[str]]]) – An iterable of iterables of reference corpus.
normalize¶ (bool) – An indication whether a general tokenization to be applied.
no_punctuation¶ (bool) – An indication whteher a punctuation to be removed from the sentences.
lowercase¶ (bool) – An indication whether to enable case-insesitivity.
asian_support¶ (bool) – An indication whether asian characters to be processed.
return_sentence_level_score¶ (bool) – An indication whether a sentence-level TER to be returned.

Return type

Union[Tensor, Tuple[Tensor, List[Tensor]]]

Returns

A corpus-level translation edit rate (TER). (Optionally) A list of sentence-level translation_edit_rate (TER) if return_sentence_level_score=True.

Example

>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> translation_edit_rate(preds, target)
tensor(0.1538)

References

[1] A Study of Translation Edit Rate with Targeted Human Annotation by Mathew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla and John Makhoul TER