Ideas for Google Summer of Code/Weighted transfer rules

At the moment Apertium transfer rules are combinations of fixed-length patterns and actions. Conflicts are "solved" by selecting the first rule that matches. The idea of this task is to allow rules to conflict and to select the most adequate rules for an input using weights.

Unlexicalised rule weights

Depend only on the category of the input words.

Lexicalised rules weights

Depend on one or more of the lemmas of the input words. e.g.

ID	Rule	Input	Output	Frequency
1	$x$ de $y$ → $x$ $y$	memoría de traducción	translation memory	90
2	$x$ de $y$ → $y$'s $x$	memoría de traducción	translation's memory	0
3	$x$ de $y$ → $x$ of $y$	memoría de traducción	memory of translation	0

So here we would have something like:

• Rule 1 (x=memoría, y=traducción, weight=1.0)
• Rule 2 (x=memoría, y=traducción, weight=0.0)
• Rule 3 (x=memoría, y=traducción, weight=0.0)

Example

Transfer rules:

ID	Rule	Input	Output
1	$x$ de $y$ → $x$ $y$	memoria de traducción	translation memory
2	$x$ de $y$ → $y$'s $x$	la hermana de mi novia	my girlfriend's sister
3	$x$ de $y$ → $x$ of $y$	el estado de la cuestión	the state of the art

Training

• Take a big corpus
• For each sentence:
  • Apply transfer rules
  • For each possible combination of transfer rules
    • Translate the sentence and score on language model
    • Each sentence gets a count 1. This count is shared between the transfer rules.

Example

	La canciller se reúne hoy con el presidente de EE UU para limar asperezas y preparar la cumbre del miércoles con Putin.
1 1	The chancellor gathers today with [the U.S. president] for mend fences and prepare [the Wednesday summit] with Putin.	-74.55	0.39
2 1	The chancellor gathers today with [the U.S.'s president] for mend fences and prepare [the Wednesday summit] with Putin.	-69.51	60.71
3 1	The chancellor gathers today with [the president of the U.S.] for mend fences and prepare [the Wednesday summit] with Putin.	-74.47	0.43
1 2	The chancellor gathers today with [the U.S. president] for mend fences and prepare [the Wednesday's summit] with Putin.	-75.02	0.25
2 2	The chancellor gathers today with [the U.S.'s president] for mend fences and prepare [the Wednesday's summit] with Putin.	-69.98	37.94
3 2	The chancellor gathers today with [the president of the U.S.] for mend fences and prepare [the Wednesday's summit] with Putin.	-74.94	0.27
1 3	The chancellor gathers today with [the U.S. president] for mend fences and prepare [the summit of the Wednesday] with Putin.	-82.88	0.0
2 3	The chancellor gathers today with [the U.S.'s president] for mend fences and prepare [the summit of the Wednesday] with Putin.	-77.84	0.01
3 3	The chancellor gathers today with [the president of the U.S.] for mend fences and prepare [the summit of the Wednesday] with Putin.	-82.80	0.0

You can then feed the fractional counts to some supervised machine learning program to get appropriate weights.

Questions

• How to calculate the paths?
  • With optimal coverage, or with just taking the LRLM and only calculating paths for rules which conflict.
• For lexicalised weights:
  • What is the function assigning cost to each lexical combination of N1 and N2?
• Could we score a rule at a time, by keeping part fixed ?

Tasks

• Implement in C++ and integrate into Apertium.

See also

• LRLM
• A long introduction to transfer rules