Syntax for MT

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Syntax for MT

• EECS 767
• Feb. 1, 2006

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Outline

• Motivation
• Syntax-based translation model
• Formalization
• Training
• Using syntax in MT
• Using multiple features
• Syntax-based features

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The IBM Models

• Word reordering
• Single words, not groups
• Conditioned on position of words
• Null-word insertion
• Uniform across position

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The Alignment Template Model

• Word Reordering
• Phrases can be reordered in any way, but tend to
  stay in same order as source.
• Reordering within phrases defined by templates
• Word Translations
• Must match up No null

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Implied Assumptions

• Word Order
• Similar to source sentence
• Translation
• Near 1-1 correspondence

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What goes wrong?

• We see many errors in machine translation when we
  only look at the word level
• Missing content words
• MT Condemns US interference in its internal
  affairs.
• Human Ukraine condemns US interference in its
  internal affairs.
• Verb phrase
• MT Indonesia that oppose the presence of foreign
  troops.
• Human Indonesia reiterated its opposition to
  foreign military presence.

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What goes wrong cont.

• Wrong dependencies
• MT , particularly those who cheat the audience
  the players.
• Human , particularly those players who cheat
  the audience.
• Missing articles
• MT , he is fully able to activate team.
• Human , he is fully able to activate the team.

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What goes wrong cont.

• Word salad
• the world arena on top of the u . s . sampla
  competitors , and since mid july has not
  appeared in sports field , the wounds heal go
  back to the situation is very good , less than a
  half hours in the same score to eliminate 62 in
  light of the south african athletes to the second
  round .

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How can we improve?

• Relying on language model to produce more
  accurate sentences is not enough
• Many of the problems can be considered
  syntactic
• Perhaps MT-systems dont know enough about what
  is important to people
• So, include syntax into MT
• Build a model around syntax
• Include syntax-based features in a model

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A New Translation Story

• You have a sentence and its parse tree
• The children at each node in the tree are
  rearranged
• New nodes may be inserted before or after a child
  node
• These new nodes are assigned a translation
• Each of the leaf lexical nodes is then translated

Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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A Syntax-based model

• Assume word order is based on a reordering of
  source syntax tree.
• Assume null-generated words happen at syntactical
  boundaries.
• (For now) Assume a word translates into a single
  word.

Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Reorder
Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Insert
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Model Thesis 2002
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Translate
Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Parameters

• Reorder (R) child node reordering
• Can take any possible child node reordering
• Defines word order in translation sentence
• Conditioned on original child node order
• Only applies to non-leaf nodes

Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Parameters cont.

• Insertion (N) placement and translation
• Left, right, or none
• Defines word to be inserted
• Place conditioned on current and parent labels
• Word choice is unconditioned

Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Parameters cont.

• Translation (T) 1 to 1
• Conditioned only on source word
• Can take on null
• Translation (T) N to N
• Consider word fertility (for 1-to-N mapping)
• Consider phrase translation at each node
• Limit size of possible phrases
• Mix phrasal w/ word-to-word translation

Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Formalization
Set of nodes in parse tree
Total probability
Assume node independence
Assume random variables are Independent of one
another and only dependent on certain features
Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Training (EM)

• Initialize all probability tables (uniform)
• Reset all counters
• For each pair in the training corpus
• Try all possible mappings of N,R, and T
• Update the counts as seen in the mappings
• Normalize the probability tables with the new
  counts
• Repeat 2-4 several times

Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Decoding

• Modify original CFG with new reordering and their
  probabilities
• Add in VP-VP X and X - word rules from N
• Add lexical rules englishWord-foreignWord
• Use the noisy-channel approach starting with a
  translated sentence
• Proceed through the parse tree using a bottom-up
  beam search keeping an N-best list of good
  partial translations for each subtree

YamadaKnight A Decoder for Syntax-based
Statistical MT 2002
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Decoding cont.
YamadaKnight A Decoder for Syntax-based
Statistical MT 2002
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Performance (Alignment)
Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Performance (Alignment) cont.

• Counting number of individual alignments
• Perfect means all alignments in a pair are correct

Yamada A Syntax-Based Statistical Translation
Model Thesis 2002
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Performance cont.

• Chinese-English BLEU scores

YamadaKnight A Decoder for Syntax-based
Statistical MT 2002
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Do we need the entire model to be based on syntax?

• Good performance increase
• Large computational cost
• Many permutations to CFG rules (120K non-lexical)
• How about trying something else?
• Add syntax-based features that look for more
  specific things

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Using Syntax in MT

• Multiple Features
• Formalization
• Baseline
• Training
• Syntax-based Features
• Shallow
• Deep

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Multiple Features (log-linear)
Calculate probability using a variety of features
parameterized by an associated weight
Find the translated sentence which maximizes the
feature function with your foreign sentence
JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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Baseline System
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Translation Final Report
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Baseline System
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Baseline Features

• Alignment template feature
• Uses simple counts

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Baseline Features

• Word selection feature
• Uses lexicon probability estimated by relative
  frequency

Additional feature capturing word reordering
within phrasal alignments
JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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Baseline Features

• Phrase alignment feature
• Measure of deviation from monotone alignment

JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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Baseline Features

• Language model feature
• Standard backing-off trigram probability
• Word/Phrase penalty feature
• Feature counting number of words in translated
  sentence
• Feature counting number of phrases in translated
  sentence
• Alignment lexicon feature
• Feature counting the number of time something
  from a given alignment lexicon is used

JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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A possible training method

• Line optimization method

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Use reranking of N-best lists

• Feature functions do not need to be integrated in
  dynamic programming search
• A feature function can arbitrarily condition
  itself on any part of English/Chinese
  sentece/parse tree/chunks
• Provides a simple software architecture
• Using a fixed set of translations allows feature
  functions to be a vector of numbers
• You are limited to improvements you see within
  the N-best lists

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Syntax-based Features

• Shallow
• POS and Chunk Tag counts
• Projected POS language model
• Deep
• Tree-to-string
• Tree-to-tree
• Verb arguments

JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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Shallow Syntax-Based Features

• POS and chunk tag count
• Low-level syntactic problems with baseline
  system. Too many articles, commas and singular
  nouns. Too few pronouns, past tense verbs, and
  plural nouns.
• Reranker can learn balanced distributions of tags
  from various features
• Examples
• Number of NPs in English
• Difference in number of NPs between English and
  Chinese
• Number of Chinese N tags translated to only non-N
  tags in English.

JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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Shallow Syntax-Based Features

• Projected POS language model
• Use word-level alignments to project Chinese POS
  tags onto the English words
• Possibly keeping relative position within Chinese
  phrase
• Possibly keeping NULLs in POS sequence
• Possibly using lexicalized NULLs from English
  word
• Use the POS tags to train a language model based
  on POS N-grams

JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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Deep Syntax-Based Features

• Tree to string
• Uses the Syntax-based model we saw previously
• Reduces computational cost by limiting size of
  reorderings
• Add in a feature for probability as defined by
  the model and the probability of the viterbi
  alignment defined by the model

JHU WS 2003 Syntax for Statistical Machine
Translation Final Report
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Deep Syntax-Based Features

• Tree to Tree
• Uses tree transformation functions similar to
  those in the tree-to-string model
• The probability of transforming a source tree
  into a target tree is modeled as a sequence of
  steps starting from the root of the target tree
  down.

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Translation Final Report
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Tree to Tree cont.

• At each level of the tree
• At most one of the current nodes children is
  grouped with the current node into a single
  elementary tree with its probability conditioned
  on the current node and its children.
• An alignment of the children of the current
  elementary tree is chosen with its probability
  conditioned on the current node an the children
  of child in the elementary tree. This is similar
  to the reorder operation in the tree-to-string
  model, but allows for node addition and removal.
• Leaf-level parameters are ignored when
  calculating probability of tree-to-tree.

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Translation Final Report
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Verb Arguments

• Idea A feature that counts the difference in the
  number of arguments to the main verb between the
  Chinese and English sentences
• Perform a breadth-first search traversal of the
  dependency trees
• Mark the first verb encountered as the main verb
• The number of arguments is equal to the number of
  its children

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Translation Final Report
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Performance

• Some things helped, some things didnt
• Is syntax useful? Necessary?

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References

• K. Yamada and K. Knight. 2001. A syntax-based
  statistical translation model. In ACL-01.
• K. Yamada. 2002. A Syntax-Based Statistical
  Translation Model. Ph.D. thesis, University of
  Southern California.
• Yamada, Kenji and Kevin Knight. 2002. A decoder
  for syntaxbased MT. In Proc. of the 40th Annual
  Meeting of the Association for Computational
  Linguistics (ACL), Philadelphia, PA.
• Franz Josef Och, Daniel Gildea, Sanjeev
  Khudanpur, Anoop Sarkar, Kenji Yamada, Alex
  Fraser, Shankar Kumar, Libin Shen, David Smith,
  Katherine Eng, Viren Jain, Zhen Jin, and Dragomir
  Radev. A smorgasbord of features for statistical
  machine translation. In Proceedings of the Human
  Language Technology Conference.North American
  chapter of the Association for Computational
  Linguistics Annual Meeting, pages 161-168, 2004.
  MIT Press.
• Franz Josef Och, Daniel Gildea, Sanjeev
  Khudanpur, Anoop Sarkar, Kenji Yamada, Alex
  Fraser, Shankar Kumar, Libin Shen, David Smith,
  Katherine Eng, Viren Jain, Zhen Jin, and Dragomir
  Radev. Final Report of the Johns Hopkins 2003
  summer workshop on Syntax for Statistical Machine
  Translation.
• Philipp Koehn, Franz Josef Och, and Daniel Marcu.
  Statistical phrase-based translation. In
  Proceedings of the Human Language Technology
  Conference/North American Chapter of the
  Association for Computational Linguistics Annual
  Meeting, 2003. MIT Press.