Recursive transfer
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).
- How do we make sure that we never get
- the glue rules would not compute anything, just allow for partial parses
- Implicit glue rules
- 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