Presentation Outline

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Presentation Outline

• Introduction

• Structured String-Tree Correspondence (SSTC)

• Synchronous Structured String-Tree Correspondence
  (SSTC)

• EBMT based on synchronous SSTC

• The Construction of a BKB Based on the
  Synchronous SSTC

• Bitext World-level Mapping (Word Alignment)

• Bitext Synchronous Parsing Technique

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The Structured String-Tree Correspondence (SSTC)
SSTC string arbitrary tree structure
correspondence
Correspondence node(X/Y)
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Example-Based Machine Translation (EBMT)
EBMT is the case-based reasoning approach to MT
EBMT uses translated examples of similar
sentences to translate a given Source sentence
into the target sentence.
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The general Architecture for EBMT
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EBMT based on synchronous SSTC.
Different senses for the word bank bank 1 a
land beside the river. bank 2 a place to keep
money. E.g The1 man2 keep1 his1 money1 in1 the1
bank2.
Replacement Combination
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Source sentence The old man picks the green lamp
up
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Set of synchronous SSTCs represents Example-base.
English sentence The lamp is off. Malay
translation Lampu itu padam.
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Source the old man picks the green lamp up
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Sub-synchronous SSTCs for the source sentence
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Selected closed example
Sub-synchronous SSTCs derived from the example
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lelaki tua itu kutip lampu hijau itu
Generation
The translation for the source sentence is
generated from the synchronous SSTC the Malay
part, which is the String in the SSTC.

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EBMT General Problems

• How to utilize more than one example to translate
  one source sentence

The construction of well-formed target language
sentences from extracted fragments of a BKB.

• lack of flexibility in representing translation
  relations between source and target substrings

The treatment of wild linguistic phenomena, which
are non-standard, e.g. crossed dependencies
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• The Construction of a BKB Based on the
  Synchronous SSTC

Based on Bitext Synchronous Parsing Technique

• BiText Text that is available in two languages.

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• Schema

Parsing POS Tagging for the English source text
Build the SSTC for Malay target text based on the
SSTC for the English source text using the word
alignment
Compile the APP output into SSTC for the English
source text
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Bitext World-level Mapping (Word Alignment)
Real texts are noisy - Fertility A single word
in the source sentence may correspond to zero,
one, two or more words in the target sentence and
vice versa.
- crossed dependencies (distortion) Where human
translators change and rearrange material so the
target output text will not flow well according
to the order of the source text.
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n Context Window Word Alignment
The correspondence between the source and the
target is denoted by an interval attached to each
subtext according to its offset in the text.
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n Context Window Word Alignment
Find the TPCs between the source and the target.
?(Bilingual dictionary)
Bilingual dictionary
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n Context Window Word Alignment
Find out the chains for all possible TPCs for a
source word.
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n Context Window Word Alignment
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• Bitext Synchronous Parsing Technique

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Apple Pie Parser (APP)

• It is a bottom-up probabilistic chart parser to
  find the parse tree for an input text (English).

• It was developed at New York University.

• The parser generates a syntactic tree in
  PennTreeBank bracketing.

• It is Free, and available to download with the
  source code.

• http//cs.nyu.edu/cs/projects/proteus/sekine

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Apple Pie Parser (APP)
The basic idea of example-based parsing is very
simple
The representation structure and the POS for the
source English is obtained
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Compile the APP output to SSTC structure
(S (NP (NPL The basic idea) (PP of (NPL
example-based parsing))) (VP is (ADJP very
simple)))
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Lexical Transfer
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The synchronous SSTC editor.
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Discussion
Thank you..