Polished RBMT system

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If you're looking for information on corpus annotation and evaluation (precision/recall), that has moved to the Midterm overview page

Measuring trimmed coverage

Apertium RBMT systems use a "trimmed" version of the base transducer for each language in the MT pair. Each transducer is trimmed to only include entries found in the bilingual dictionary (.dix). The trimmed transducers are almost always smaller than the main transducers. Measuring coverage of the trimmed transducer gives you some idea of the ceiling of MT accuracy—or, at least, the percentage of forms in a corpus the MT system can analyse, regardless of translation accuracy.

Measuring trimmed coverage is just the same as measuring coverage, but with the appropriate "trimmed" transducer (e.g., xyz-abc.automorf.bin). You'll need to use the coverage-ltproc script instead of coverage-hfst.

The assignment

This assignment is due at the end of week 12 (this semester, at the end of the day on Friday, 29 April 2022, before midnight).

  1. Before you begin, make sure all previous assignments are done, and add a "structural_transfer" tag to your transducer repositories and your translation pair repository/ies to mark the end of previous assignments.
  2. Set up some new corpora based on existing ones:
    • Combine and merge your sentences and tests corpora so you [hopefully, but not necessarily] have a new longer parallel corpus. Name the files abc.longer.txt and xyz.longer.txt.
    • Make a large monolingual corpus of a bunch of raw text in your language. The more the better. This step may simply consist of you cleaning up and/or combining the existing corpora from the initial corpus assembly assignment. See if you can get it over 100K words. The bigger this corpus is the better. Call it abc.corpus.large.txt (in your monolingual corpus repo) and add notes to your MANIFEST file about where the text comes from.
  3. Expand your MT pair in at least three of the following ways for each translation direction that you're working on, listing in a "Final evaluation" section on the language pair's wiki page what you did (move existing evaluation sections under a new section called "Initial evaluation"), and for every rule (for all of the following except adding stems), list an example of what output was improved.
    • At least 100 more stems in the bilingual dictionary (and monolingual dictionaries as needed). This counts for both translation directions, if you are working on two-way translation. (If you've automated creation of your bilingual dictionary, then you can use this task to clean up 100 stems—at least 50 entries in your bilingual dictionary should be different than before—and properly categorise those 100 stems into the monolingual dictionary.)
    • Expand your morphology to cover at least 5 more elements of some paradigm(s). This can be anything from additional verb or noun morphology, to adding all the forms of all the determiners (articles, demonstratives, etc.), to implementing nominal morphology on adjectives (e.g., if your language allows adjectives to be substantivised, which you'll want to add a tag for too).
    • At least 4 more twol rules that make your (analysis and) generation cleaner for an additional way.
    • At least 3 new disambiguation rules that make the output of your tagger more accurate.
    • At least 2 new lexical selection rules that make more of the right stems transfer over.
    • At least 2 new transfer rules that make more of the output of your MT system closer to an acceptable target translation.
  4. When you are done with the above:
    • Document which of the above options you completed on the pair's wiki page (in a section like "Additions"). You don't have to list the words or rules you added, but do list that you added n words or n transfer rules or the like.
    • Add a "polished RBMT system" tag to your repo.
    • document the following measures in the "Final evaluation" section of the pair's wiki page:.
      • For your monolingual transducer:
        • Updated precision and recall against the (updated) eval.test and eval.gold files (see Midterm overview#Evaluating your transducer),
        • Coverage over the large corpus,
        • The number of words in the large corpus,
        • The number of stems in the transducer.
      • For MT in the direction(s) you developed (abc-xyz and potentially xyz-abc):
        • WER and PER over longer corpus.
        • The proportion of stems translated correctly in the longer corpus.
        • Trimmed coverage over longer and large corpora.
        • The number of tokens in longer and large corpora.