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).
  • 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

• CKY (bottom-up)
• LALR(1) (bottom-up)
• GLR (bottom-up)
• Earley (top-down)

Usage

$ svn co https://svn.code.sf.net/p/apertium/svn/branches/transfer4

$ cd transfer4

$ cd eng-kaz

$ make

Files

• eng-kaz.grammar: Transfer grammar file for English→Kazakh
• eng-kaz.t1x: Categories (terminals) and attributes for English→Kazakh

Apply the transfer grammar

$ cat input/input.01.txt | ./eng-kaz.parser 
^you<prn><subj><p2><mf><sp>/сен<prn><pers><subj><p2><mf><sp>$ ^Kazakhstan<np><top><sg>/Қазақстан<np><top><nom>$ ^to<pr>/$ 
^go<vblex><past>/бар<v><iv><past>$ ^that<cnjsub>/$ ^I<prn><subj><p1><mf><sg>/Мен<prn><pers><subj><p1><mf><sg>$ 
^know<vblex><pres>/біл<v><tv><pres>$ ^.<sent>/.<sent>$ 

Print out the source tree

$ cat input/input.01.txt | ./eng-kaz.parser -s -p >/dev/null
(S (S1 (PRNS (subj_pron (^I<prn><subj><p1><mf><sg>/Мен<prn><pers><subj><p1><mf><sg>$))) 
(SV (V (pers_verb (^know<vblex><pres>/біл<v><tv><pres>$))))) (Ssub (cnjsub (^that<cnjsub>/$)) 
(S1 (PRNS (subj_pron (^you<prn><subj><p2><mf><sp>/сен<prn><pers><subj><p2><mf><sp>$))) 
(SV (V (pers_verb (^go<vblex><past>/бар<v><iv><past>$))) (SP (prep (^to<pr>/$)) 
(SN1 (SN (N (nom (^Kazakhstan<np><top><sg>/Қазақстан<np><top><nom>$))))))))) (X (sent (^.<sent>/.<sent>$))))

Print out the target tree

$ cat input/input.01.txt | ./eng-kaz.parser -p >/dev/null
(S (Ssub (S1 (PRNS (subj_pron (^you<prn><subj><p2><mf><sp>/сен<prn><pers><subj><p2><mf><sp>$))) 
(SV (SP (SN1 (SN (N (nom (^Kazakhstan<np><top><sg>/Қазақстан<np><top><nom>$))))) (prep (^to<pr>/$))) 
(V (pers_verb (^go<vblex><past>/бар<v><iv><past>$))))) (cnjsub (^that<cnjsub>/$))) 
(S1 (PRNS (subj_pron (^I<prn><subj><p1><mf><sg>/Мен<prn><pers><subj><p1><mf><sg>$))) 
(SV (V (pers_verb (^know<vblex><pres>/біл<v><tv><pres>$))))) (X (sent (^.<sent>/.<sent>$))))

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

• User:Mlforcada/Robust LR for Transfer
• 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
• http://www.cs.cmu.edu/~./alavie/papers/thesis.pdf
• http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-743.pdf

See also

• https://svn.code.sf.net/p/apertium/svn/branches/transfer4

External links

• CFG tool
• LOGON: Parse with the ERG