Transformationbased errordriven learning TBL

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Transformation-based error-driven learning (TBL)

• LING 572
• Fei Xia
• 1/19/06

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Outline

• Basic concept and properties
• Relation between DT, DL, and TBL
• Case study

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Basic concepts and properties
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TBL overview

• Introduced by Eric Brill (1992)
• Intuition
• Start with some simple solution to the problem
• Then apply a sequence of transformations
• Applications
• Classification problems
• Other kinds of problems e.g., parsing

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TBL flowchart
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Transformations

• A transformation has two components
• A trigger environment e.g., the previous tag is
  DT
• A rewrite rule change the current tag from MD to
  N
• If (prev_tag T) then MD ? N
• Similar to a rule in decision tree, but the
  rewrite rule can be complicated (e.g., change a
  parse tree)
• ? a transformation list is a processor and not
  (just) a classifier.

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Learning transformations

• Initialize each example in the training data with
  a classifier
• Consider all the possible transformations, and
  choose the one with the highest score.
• Append it to the transformation list and apply it
  to the training corpus to obtain a new corpus.
• Repeat steps 2-3.
• ? Steps 2-3 can be expensive. Various ways that
  try to solve the problem.

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Using TBL

• The initial state-annotator
• The space of allowable transformations
• Rewrite rules
• Triggering environments
• The objective function minimize error rate
  directly.
• for comparing the corpus to the truth
• for choosing a transformation

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Using TBL (cont)

• Two more parameters
• Whether the effect of a transformation is visible
  to following transformations
• If so, whats the order in which transformations
  are applied to a corpus?
• left-to-right
• right-to-left

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The order matters

• Transformation
• If prev_labelA then change the cur_label from A
  to B.
• Input A A A A A A
• Output
• Not immediate results A B B B B B
• Immediate results, left-to-right A B A B A B
• Immediate results, right-to-left A B B B B B

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Relation between DT, DL, and TBL
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DT and TBL

• DT is a subset of TBL
• (Proof)
• when depth(DT)1

• Label with S
• If X then S ? A
• S ? B

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DT is a subset of TBL
Depthn
L1 Label with S L1
L2 Label with S L2
Depthn1
Label with S If X then S ? S S ? S L1 L2
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DT is a subset of TBL
Label with S If X then S ? S S ? S L1
(renaming X with X) L2 (renaming X with
X) X ? X X ? X
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DT is a proper subset of TBL

• There exists a problem that can be solved by TBL
  but not a DT, for a fixed set of primitive
  queries.
• Ex Given a sequence of characters
• Classify a char based on its position
• If pos 4 0 then yes else no
• Input attributes available previous two chars

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• Transformation list
• Label with S A/S A/S A/S A/S A/S A/S A/S
• If there is no previous character, then S? F
• A/F A/S A/S A/S A/S A/S A/S
• If the char two to the left is labeled ith F,
  then S? F
• A/F A/S A/F A/S A/F A/S A/F
• If the char two to the left is labeled with F,
  then F?S
• A/F A/S A/S A/S A/F A/S A/S
• F ? yes
• S ? no

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DT and TBL

• TBL is more powerful than DT
• Extra power of TBL comes from
• Transformations are applied in sequence
• Results of previous transformations are visible
  to following transformations.

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DL and TBL

• DL is a proper subset of TBL.
• In two-class TBL
• (if q then y ? y) ? (if q then y)
• If multiple transformations apply to an example,
  only the last one matters

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Two-class TBL ? DL ?

• Two-class TBL ? DL
• Replace if q then y?y with if q then y
• Reverse the rule order
• DL ? two-class TBL
• Replace if q then y with if q then y?y
• Reverse the rule order
• ? does not hold for dynamic problems
• Dynamic problem the anwers to questions are not
  static
• Ex in POS tagging, when the tag of a word is
  changed, it changes the answers to questions for
  nearby words.

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DT, DL, and TBL (summary)

• K-DT is a proper subset of k-DL.
• DL is a proper subset of TBL.
• Extra power of TBL comes from
• Transformations are applied in sequence
• Results of previous transformations are visible
  to following transformations.
• TBL transforms training data. It does not split
  training data.
• TBL is a processor, not just a classifier

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Case study
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TBL for POS tagging

• The initial state-annotator most common tag for
  a word.
• The space of allowable transformations
• Rewrite rules change cur_tag from X to Y.
• Triggering environments (feature types)
  unlexicalized or lexicalized

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Unlexicalized features

• t-1 is z
• t-1 or t-2 is z
• t-1 or t-2 or t-3 is z
• t-1 is z and t1 is w

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Lexicalized features

• w0 is w.
• w-1 is w
• w-1 or w-2 is w
• t-1 is z and w0 is w.

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TBL for POS tagging (cont)

• The objective function tagging accuracy
• for comparing the corpus to the truth
• For choosing a transformation choose the one
  that results in the greatest error reduction.
• The order of applying transformations
  left-to-right.
• The results of applying transformations are not
  visible to other transformations.

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Learned transformations
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Experiments
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Uncovered issues

• Efficient learning algorithms
• Probabilistic TBL
• Top-N hypothesis
• Confidence measure

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TBL Summary

• TBL is more powerful than DL and DT.
• Transformations are applied in sequence
• It can handle dynamic problems well.
• TBL is more than a classifier
• Classification problems POS tagging
• Other problems e.g., parsing
• TBL performs well because it minimizes errors
  directly.
• Learning can be expensive ? various methods