This document discusses building an Urdu-to-English machine translation system for NIST MT Eval 2008 using only parallel training data released by NIST/LDC consisting of around 100k words. It notes that the most important additional annotation that could be added to the training data is English parse trees. The document outlines that without Urdu morphology analysis, the system will be a full-form to full-form MT system. It also questions what type of transfer rule development is allowed based on the training conditions. Finally, it lists the main components needed as a bilingual lexicon, base NP translation tables, transfer grammar rules, and an English language model, all of which will be extracted from the parallel corpus using tools like GIZ

1. Urdu-to-English Stat-XFER system
for NIST MT Eval 2008
• Resources:
– “Constrained” training condition: only parallel training
data released by NIST/LDC on DVD
– We believe this is the LDC “language pack” for URDU
developed under NSA funding
– 100K words? Includes our recent 3100 sentence
Elicitation Corpus (~10K words?)
– Annotations to the training data can be added and
distributed to all participants
• Most important for us: English parse trees
• Any other annotations? POS-tags?

2. Training Resources
• Implications:
– No morphology analyzer on the Urdu side
– Full-form to full-form MT system?
– Collapse entries on English side only and plug
in English morphology generator?
– Is manual transfer rule development allowed
under the training rules? Only rules that are
extracted automatically from the parallel data?
– Human filtering of automatically extracted
rules?

3. Building the Urdu-to-English
system
• Main “pieces” that we need:
– Bilingual word-to-word translation lexicon
– Base NPs translation tables
– XFER grammar rules
– English Language Model

4. Bilingual Translation Lexicon and
Base NPs
• Extracted from the parallel corpus
• Bootstrap with GIZA++ word alignment (IBM-1 or
IBM-3)
• Filtering of the resulting word-to-word
translations
– Combining alignments from both directions
– Applying various threshold methods
• New iterative method with extraction of base
NPs (Vamshi):
– Need English parses for the entire parallel corpus
– We will have accurate “gold” parses for the EC portion
of the corpus