Translation Edit Rate (TER)

Module Interface

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

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

>>> from torchmetrics.text import TranslationEditRate
>>> 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)

plot(val=None, ax=None)

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> from torchmetrics.text import TranslationEditRate
>>> metric = TranslationEditRate()
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> from torchmetrics.text import TranslationEditRate
>>> metric = TranslationEditRate()
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

Functional Interface

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

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