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Handle USFM and Text corpora separately in pre-processing #894

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110 changes: 68 additions & 42 deletions src/ServiceToolkit/src/SIL.ServiceToolkit/Services/ParallelCorpusService.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -153,54 +153,80 @@ public async Task PreprocessAsync(
ignoreUsfmMarkers ??= [];

bool parallelTrainingDataPresent = false;
List<Row> keyTermTrainingData = new();
List<Row> keyTermTrainingData = [];

// Iterate over USFM and Text training corpora separately.
// This is not only because they use different keys, but if we have text corpora
// with scripture corpora, we don't want to exclude the text corpora from training.
foreach (bool isScripture in new bool[] { true, false })
{
// Create source and target arrays of text corpora filtered for training
// based on the filters specified in the associated monolingual corpora
ITextCorpus[] sourceTrainingCorpora =
[
.. corpusBundle.SourceTextCorpora.SelectMany(c =>
c.TextCorpora.Where(tc => isScripture ? tc.IsScripture() : !tc.IsScripture())
.Select(tc => FilterTrainingCorpora(c.MonolingualCorpus, tc))
),
];

ITextCorpus[] targetTrainingCorpora =
[
.. corpusBundle.TargetTextCorpora.SelectMany(c =>
c.TextCorpora.Where(tc => isScripture ? tc.IsScripture() : !tc.IsScripture())
.Select(tc => FilterTrainingCorpora(c.MonolingualCorpus, tc))
),
];

// To support mixed source, collapse multiple source text corpora into one text corpus
// by randomly interlacing content from each of the source text corpora
ITextCorpus sourceTrainingCorpus = sourceTrainingCorpora.ChooseRandom(Seed);
if (sourceTrainingCorpus.IsScripture())
{
// Filter out all non-scripture; we only train on scripture content
sourceTrainingCorpus = sourceTrainingCorpus.Where(IsScriptureRow);
}

// Create source and target arrays of text corpora filtered for training
// based on the filters specified in the associated monolingual corpora
ITextCorpus[] sourceTrainingCorpora = corpusBundle
.SourceTextCorpora.SelectMany(c =>
c.TextCorpora.Select(tc => FilterTrainingCorpora(c.MonolingualCorpus, tc))
)
.ToArray();
// Instead of interlacing rows from the target text corpora randomly, just take the
// text row from the first target text corpus that has content for that row
ITextCorpus targetTrainingCorpus = targetTrainingCorpora.ChooseFirst();
if (targetTrainingCorpus.IsScripture())
{
// Filter out all non-scripture; we only train on scripture content
targetTrainingCorpus = targetTrainingCorpus.Where(IsScriptureRow);
}

ITextCorpus[] targetTrainingCorpora = corpusBundle
.TargetTextCorpora.SelectMany(c =>
c.TextCorpora.Select(tc => FilterTrainingCorpora(c.MonolingualCorpus, tc))
)
.ToArray();
// Align source and target training data
ParallelTextRow[] trainingRows =
[
.. sourceTrainingCorpus.AlignRows(targetTrainingCorpus, allSourceRows: true, allTargetRows: true),
];

// To support mixed source, collapse multiple source text corpora into one text corpus
// by randomly interlacing content from each of the source text corpora
ITextCorpus sourceTrainingCorpus = sourceTrainingCorpora.ChooseRandom(Seed);
if (sourceTrainingCorpus.IsScripture())
{
// Filter out all non-scripture; we only train on scripture content
sourceTrainingCorpus = sourceTrainingCorpus.Where(IsScriptureRow);
// After merging segments across ranges, run the 'train' preprocessing function
// on each training row and record whether any parallel training data was present
foreach (Row row in CollapseRanges(trainingRows))
{
await train(row, TrainingDataType.Text);
if (!parallelTrainingDataPresent && row.SourceSegment.Length > 0 && row.TargetSegment.Length > 0)
{
parallelTrainingDataPresent = true;
}
}
}

// Instead of interlacing rows from the target text corpora randomly, just take the
// text row from the first target text corpus that has content for that row
ITextCorpus targetTrainingCorpus = targetTrainingCorpora.ChooseFirst();
if (targetTrainingCorpus.IsScripture())
// Get the target corpus from the training corpora.
// This is across Scripture and non-Scripture corpora
ITextCorpus[] targetCorpora =
[
.. corpusBundle.TargetTextCorpora.SelectMany(c =>
c.TextCorpora.Select(tc => FilterTrainingCorpora(c.MonolingualCorpus, tc))
),
];
ITextCorpus targetCorpus = targetCorpora.ChooseFirst();
if (targetCorpus.IsScripture())
{
// Filter out all non-scripture; we only train on scripture content
targetTrainingCorpus = targetTrainingCorpus.Where(IsScriptureRow);
}

// Align source and target training data
ParallelTextRow[] trainingRows = sourceTrainingCorpus
.AlignRows(targetTrainingCorpus, allSourceRows: true, allTargetRows: true)
.ToArray();

// After merging segments across ranges, run the 'train' preprocessing function
// on each training row and record whether any parallel training data was present
foreach (Row row in CollapseRanges(trainingRows))
{
await train(row, TrainingDataType.Text);
if (!parallelTrainingDataPresent && row.SourceSegment.Length > 0 && row.TargetSegment.Length > 0)
{
parallelTrainingDataPresent = true;
}
targetCorpus = targetCorpus.Where(IsScriptureRow);
}

if (useKeyTerms)
Expand Down Expand Up @@ -255,7 +281,7 @@ public async Task PreprocessAsync(
{
sourceInferencingCorpus,
targetInferencingCorpus,
targetTrainingCorpus,
targetCorpus,
}.AlignMany([true, false, false]);

foreach ((Row row, bool isInTrainingData) in CollapseInferencingRanges(inferencingCorpus.ToArray()))
Expand Down
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