Abstract
Extracting parallel units (e.g. sentences or phrases) from comparable corpora in order to enrich existing statistical translation models is an avenue that has attracted a lot of research in recent years. There are experiments that convincingly show how parallel sentences extracted from comparable corpora are able to improve statistical machine translation (SMT). Yet, the existing body of research on the subject does not take into account the degree of comparability of the corpus being processed nor the computation time that it takes to extract translational similar pairs from a corpus of a given size. We will show that the performance of a parallel unit extractor crucially depends on the degree of comparability, such that it is more difficult to mine for parallel data in a weakly comparable corpus than a strongly comparable corpus.
Most of the research in parallel data mining from comparable corpora focusses on parallel sentence mining, but parallel phrase mining (i.e. sub-sentential fragments) is of equal importance, because it can be more robust in the presence of weakly comparable corpora that usually do not contain whole translated sentences. We will present different approaches to parallel sentence and phrase mining from comparable corpora developed in the ACCURAT project, and we will evaluate them both in terms of absolute measures (e.g., P, R and F1) and with respect to their ability to generate significant improvements of the BLEU scores of a statistical translation system. Comprehensive testing of these algorithms in the context of statistical machine translation will be undertaken in Chap. 6.
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Notes
- 1.
With the possible exception of parallelising the computations.
- 2.
Or ‘alignments’ or ‘pairs.’ These terms will be used with the same meaning throughout this section.
- 3.
We did not attempt to find the mathematical maximum of the expression from Eq. (5.7), and we realise that the consequence of this choice and of the greedy search procedure is not finding the true optimum.
- 4.
- 5.
We keep functional words lists for all languages.
- 6.
- 7.
We experimented with different power values for the cohesion score. We had the best results with ½ (the square root).
- 8.
But we acknowledge the fact that the probability of a sentence pair being parallel as computed by the classifier of Munteanu and Marcu is a proper model of parallelism.
- 9.
To obtain the dictionaries mentioned throughout this subsection, we have applied GIZA++ on the JRC Acquis corpus (Steinberger et al. 2006).
- 10.
For two source and target words, if the pair is not in the dictionary, we use a 0 to 1 normalised version of the Levenshtein distance in order to assign a ‘translation probability’ based on string similarity alone. If the source and target words are similar above a certain threshold (experimentally set to 0.7), we consider them to be translations.
- 11.
Mostly from the News domain for all language pairs.
- 12.
When an example occurs multiple times with both labels, we retain all the occurrences of the example with the most frequent label and remove all the conflicting occurrences.
- 13.
- 14.
For each parallel sentence, 2 noise sentences were added.
- 15.
- 16.
- 17.
- 18.
These phrases are extracted with the SVM margin that maximises the F-measure, see the ‘Classifier evaluation’ subsection for details.
- 19.
Koehn (2004) reports that an increase of 1% in BLEU score is a significant improvement.
- 20.
And, if it is a set, no source phrase is repeated.
- 21.
The probability threshold over which all generated parallel pairs is correct is dependent on the type of document pairs. For the English-Romanian pair of parallel documents on which we tested, at least 0.5 is guaranteed to indicate perfect parallelism (we have determined that by manually inspecting the output).
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Chapter editors: Radu Ion and Dan Tufiș
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Aker, A. et al. (2019). Mapping and Aligning Units from Comparable Corpora. In: Skadiņa, I., Gaizauskas, R., Babych, B., Ljubešić, N., Tufiş, D., Vasiļjevs, A. (eds) Using Comparable Corpora for Under-Resourced Areas of Machine Translation. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-99004-0_5
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