Add AQUA as word alignment engine

[johnml1135]

This is to add the AQUA missing words assessment to Serval.

Implementation option 1 (fully modular):

• Use a new folder: Assessment
• Move some stuff from machine to ServiceToolkit, especially the ClearML stuff
• Use the same engine/job combo project

Implementation option 2 (start combining):

• Rename Machine folder to Backend/Calculations/NLP_Engines, etc.
• Combine Machine Engine and Job into one deployment
• Add AQUA assessment to that deployment
• Create a few more projects (as needed) to hold the unique aspects of the various engines (translation and assessment).

[johnml1135]

Do the following to the existing files:

• Keep Serval and the other primary folders as is.
• Rename Machine as Backend
  • Rename Serval.Machine.Shared as Serval.Backend.Shared
  • Combine Serval.Machine.EngineServer and Serval.Machine.JobServer into Serval.Backend.BackendServer
  • Add Aqua assessments (and future things as well) to that backend server
  • Pull out parts of Serval.Backend.Shared into Serval.Backend.Machine - the parts that are specific to translation such as:
  • TranslationEngine
  • TrainSegmentPair
  • Corpus
  • Anything with Smt, Nmt or Thot
  • Create a new project called Serval.Backend.Aqua and include
  • The same job, build, ClearML, etc. aspects from Machine as is
  • A new job runner for AQUA word alignment job
  • Two assessments from the data:
    • A formal equivalence assessment, that is, one number per verse/segment
    • A source word assessment, giving a number associated with each source word per verse
  • One engine can create both assessments
  • Other "reference AQUA engines" can be created and then compared against the primary AQUA engine
  • The API Layer hands down "use this corpora, here are your reference engines" and the AQUA Backend passes back "here are the fully calculated scores to pass to Lynx".
  • Lynx then determines the relevance and meaning of the two AQUA metrics

@ddaspit, what do you think?

[johnml1135]

Ok - we will abandon the assessment API for right now and make a word alignment API.

• Call it AQUA enhanced word alignment
• Add thot word alignment
• Add the word alignment to the existing EngineServer and JobServer - but keep those docker containers separate.
• Add a new Serval.AQUA.WordAlignment project under Machine (if needed - if we do the Z-score in Serval).
• Start with planning out the new API layer and adding the machine.py normal word alignment.

[johnml1135]

@ddaspit, what do you think - the basic refactoring would be:

• There are base "Engine", "Corpora", "Job" classes
• Inheritance tree:
  • Engine - Name, Id, Revision, etc.
  • TrainingEngine (Source and Target Corpus)
    • TranslationEngine - no changes
    • WordAlignmentEngine - no changes
  • AssessmentEngine
  • Corpus - a single language and set of files
  • TrainingCorpus - Source and Target sets of files
  • FilteredCorpus - a corpus reference with textIds and ScriptureRef filtering
  • Job - state,
  • TrainingBuildJob - IsPersisted, FilteredCorpus etc.
    • TranslationBuildJob - add pretranslations
    • WordAlignmentBuildJob - add word alignments
  • AssessmentJob - FilteredCorpus

Keep all API and database things the same. This is refactoring with 0 other changes. Just get ready for WordAlignment, don't add it yet.

[johnml1135]

@ddaspit - How should we represent word alignments at the Serval API layer? Here is the interface that I am assuming:

• When training an engine, you can also have Serval align a portion of the training data, or other data
• After training, the user can pass a source and target segment (with an optional scripture reference) to be aligned
• Data back needs to include:
  • The aligned words
  • A single metric signifying the quality of the alignment
  • Indication of the tokenization of the source and target sentences

Options:

1. Just take word pairs and a score -> John:Juan:0.89
2. Just take number pairs and a score -> 7:8:0.89
3. Add both by using a "|" -> 7|John:8|Juan:0.89
4. Use json to add the tokenization:

   {
   source_tokenization: ["His", "name", "is", "John"],
   target_tokenization: ... ,
   alignment: 1:1:0.89, 2:2:0.7546
   }

[ddaspit]

You should take a look at the TranslationResult model for inspiration. We will probably want a subset of the properties in that model, specifically SourceTokens, TargetTokens, and Alignment.