Course Summary

1
Course Summary

• LING 575
• Fei Xia
• 03/06/07

2
Outline

• Introduction to MT 1
• Major approaches
• SMT 3
• Transfer-based MT 2
• Hybrid systems 2
• Other topics

3
Introduction to MT
4
Major challenges

• Translation is hard.
• Getting the right words
• Choosing the correct root form
• Getting the correct inflected form
• Inserting spontaneous words
• Putting the words in the correct order
• Word order SVO vs. SOV,
• Unique constructions
• Divergence

5
Lexical choice

• Homonymy/Polysemy bank, run
• Concept gap no corresponding concepts in another
  language go Greek, go Dutch, fen sui, lame duck,
  
• Coding (Concept ? lexeme mapping) differences
• More distinction in one language e.g., kinship
  vocabulary.
• Different division of conceptual space

6
Major approaches

• Transfer-based
• Interlingua
• Example-based (EBMT)
• Statistical MT (SMT)
• Hybrid approach

7
The MT triangle
Meaning
(interlingua)

Synthesis
Analysis
Transfer-based
Phrase-based SMT, EBMT
Word-based SMT, EBMT
word
Word
8
Comparison of resource requirement
9
Evaluation

• Unlike many NLP tasks (e.g., tagging, chunking,
  parsing, IE, pronoun resolution), there is no
  single gold standard for MT.
• Human evaluation accuracy, fluency,
• Problem expensive, slow, subjective,
  non-reusable.
• Automatic measures
• Edit distance
• Word error rate (WER), Position-independent WER
  (PER)
• Simple string accuracy (SSA), Generation string
  accuracy (GSA)
• BLEU

10
Major approaches
11
Word-based SMT

• IBM Models 1-5
• Main concepts
• Source channel model
• Hidden word alignment
• EM training

12
Source channel model for MT
P(E)
P(F E)
Fr sent
Eng sent
Noisy channel

• Two types of parameters
• Language model P(E)
• Translation model P(F E)

13
Modeling p(F E) with alignment
14
Modeling
Model 1
Model 2

• Parameters
• Length prob P(m
  l)
• Translation prob t(fj
  ei)
• Distortion prob (for Model 2) d(i j, m, l)

15
Training

• Model 1

16
Finding the best alignment
Given E and F, we are looking for
Model 1
17
Clump-based SMT

• The unit of translation is a clump.
• Training stage
• Word alignment
• Extracting clump pairs
• Decoding stage
• Try all segmentations of the src sent and all the
  allowed permutations
• For each src clump, try TopN tgt clumps
• Prune the hypotheses

18
Transfer-based MT

• Analysis, transfer, generation
• Example (Quirk et al., 2005)
• Parse the source sentence
• Transform the parse tree with transfer rules
• Translate source words
• Get the target sentence from the tree
• Translation as parsing
• Example (Wu, 1995)

19
Hybrid approaches

• Preprocessing with transfer rules (Xia and
  McCord, 2004), (Collins et al, 2005)
• Postprocessing with taggers, parsers, etc JHU
  2003 workshop
• Hierarchical phrase-based model (Chiang, 2005)

20
Other topics
21
Other issues

• Resources
• MT for Low density languages
• Using comparable corpora and wikipedia
• Special translation modules
• Identifying and translating name entities and
  abbreviations

22
To build an MT system (1)

• Gather resources
• Parallel corpora, comparable corpora
• Grammars, dictionaries,
• Process data
• Document alignment, sentence alignment
• Tokenization, parsing,

23
To build an MT system (2)

• Modeling
• Training
• Word alignment and extracting clump pairs
• Learning transfer rules
• Decoding
• Identifying entities and translating them with
  special modules (optional)
• Translation as parsing, or parse transfer
  translation
• Segmenting src sentence, replace src clump with
  target clump,

24
To build an MT system (3)

• Post-processing
• System combination
• Reranking
• Using the system for other applications
• Cross-lingual IR
• Computer-assisted translation
• .

25
Misc

• Grades
• Assignments ( hw1-hw3) 30
• Class participation 20
• Project
• Presentation 25
• Final paper 25