User:Francis Tyers/An MT system in one thousand steps

The idea of this page is to split the creation of a new language pair into bite-sized chunks that could be done in around two-hours or less by an experienced developer. One use of the page might be to organise work into tasks for the Google Code-in or to parallelise development between multiple people.

Research

• Amass resources (1 task)
  • Find a grammar of language X and of language Y
  • Find a bilingual dictionary X-Y
  • Find bilingual dictionaries X-Z and Y-Z
  • Find 1-3 large monolingual corpora of language X and language Y
  • Find a parallel corpus of language X and language Y

Morphological analysers (~200 tasks)

For languages X and Y:

• Add closed categories
  • Add adpositions and conjunctions (1 task)
  • Add determiners (1 task)
  • Add pronouns (1 task)
  • Add numerals (1 task)
    • At least 1-100 leaving out compositional numerals
• Categorise and lemmatise words by frequency
  • Create frequency lists from your corpora
  • Categorise words (15 tasks)
  • Lemmatise words (15 tasks)
• Add open categories by frequency
  • Add nouns (26 tasks)
  • Add proper nouns (16 tasks)
  • Add adjectives (15 tasks)
  • Add adverbs (3 tasks)
  • Add verbs (20 tasks)

For adding the open categories, we assume around 100 words per task.

Bilingual dictionary

• Add closed categories (1 task)
• Morphologically analyse and word align parallel corpus
  • Extract bilingual dictionary candidates
  • Proofread and add open category candidates by frequency
• Take freely available dictionaries online
  • Convert to lttoolbox format
    • Add and check nouns (2 tasks)
    • Add and check verbs (2 tasks)
    • Add and check adjectives (2 tasks)
    • Add and check adverbs (2 tasks)

Disambiguation

• Make a list of most frequent ambiguities (both lemma ambiguities and POS ambiguities) (1 task)
• Write disambiguation rules for most frequent POS+lemma ambiguities (15 tasks)
• Write disambiguation rules for most frequent POS ambiguities (15 tasks)
• Train statistical POS tagger (1 task)
• Find bad POS disambiguation leading to bad translation (15 tasks)
• Write rules to fix bad POS disambiguation (15 tasks)

Lexical selection

• POS tag and word align parallel corpus (1 task)
• Extract default translation rules (1 task)
• Extract context rules (maximum entropy) (1 task)
• Make a list of most frequent ambiguities (both lemma ambiguities and POS ambiguities) (1 task)
• Write lexical selection rules for frequent ambiguities (10 tasks)

Transfer rules

• Write a contrastive grammar