Recursive transfer

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Todo

  • Make the parser output optionally original parse tree (SL syntax) and target parse tree (TL syntax).
  • Attribute structures. These are defined in typical .t1x format with def-attrs
  • Make the parser robust — we should never get parse errors, though our trees may be mangled.

Process

The parser has two trees, both are built simultaneously:

  • The source tree is parser-internal
  • The target tree is the "abstract syntax tree".

When a sentence terminal (S) is reached, the target tree is traversed and printed out.

Questions

  • What to do with a parse-fail.
    • Implicit glue rules
      • How do we make sure that we never get Syntax error (e.g. really robust glue rules).
    • the glue rules would not compute anything, just allow for partial parses
  • How about unknown words...
    • they would be some non-terminal UNK that would be glued  by the all-encompassing glue rule from above.
  • Ambiguous grammars -> can be automatically disambiguated ?
    • Learn shift/reduce using target-language information ?
  • Converting right-recursive to left-recursive grammars.
  • How to apply macros in rules which have >1 non-terminal.
  • What on earth to do with blanks / formatting...
  • Do we try and find syntactic relations in the transfer, or do we pre-annotate (e.g. with CG) then use the tags from CG to constraint the parser?
  • Can/should we do unification in the grammar (e.g. to avoid rules like SN -> adj n matching when adj.G and n.G are not the same)?
    If a language uses CG, the rule SN -> @A→ @N would only match where CG mapped @A→ (and CG can do unification with less trouble, not mapping @A→ where gender differs)
    • However, if we are to propagate attributes up the tree as well, it makes sense to have unification as well, so we can say NP[gen=X] -> D[gen=X] N[gen=X]
  • Should the transfer allow for >1 possible TL translation ? to allow 'lexical selection' inside transfer as well as outside ?
  • Can we learn transfer grammars from aligned treebanks ?

Algorithms

References

  • Prószéky & Tihanyi (2002) "MetaMorpho: A Pattern-Based Machine Translation System"
  • White (1985) "Characteristics of the METAL machine translation system at Production Stage" (§6)
  • Slocum (1982) "The LRC Machine translation system: An application of State-of-the-Art ..." (p.18)

Further reading

  • MUHUA ZHU, JINGBO ZHU and HUIZHEN WANG (2013) "Improving shift-reduce constituency parsing with large-scale unlabeled data". Natural Language Engineering . October 2013, pp. 1--26

See also

External links