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Learning for Structured Prediction Overview of the Material. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A A A A A A A. Outline. Type of structures considered Generative vs Discriminative

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learning for structured prediction overview of the material

Learning for Structured PredictionOverview of the Material

TexPoint fonts used in EMF.

Read the TexPoint manual before you delete this box.: AAAAAAAAAAAAAAAAA

outline
Outline
  • Type of structures considered
  • Generative vs Discriminative
  • Global discriminative vs local discriminative
  • Decoding:
    • at testing vs at learning
    • methods for decoding
  • Predefined features vs latent features
  • I will use red italic to have illustration of methods; oversimplify some points
types of structures
Types of Structures
  • Sequences:
    • Chain CRFs, HMMs, (chain type) M3Ns, ....
  • Trees:
    • Constituency trees: weighted CFGs (including LA-PCFGs), left-corner/shift-reduce parsers (the MaxEnt parser, ISBN parser,...)
    • Dependency structures: MST-parser, Nivre’s shift reduce parser, ...
  • Rankings
    • Prank (today)
  • Not considered: DAGs (e.g., some semantic representations), Bipartite graphs (machine translation), or more general graphs ...
generative vs discriminative
Generative vs Discriminative
  • Discriminative: CRFs, MEMM, Structured Perceptron, Max-Margin Markov Networks (M3Ns),...
    • Learn mapping from to , so that expected error is minimal
    • Pros:
      • model what you actually care about
      • complex features of x are easy to integrate
      • different errors can be considered
      • less assumptions (and therefore, better asymptotic performance)
  • Generative
    • Score how likely is the combination of input and output
    • Pros:
      • easier to learn (if everything is observable – ML parameters are normalized counts)
      • “cleaner” semi-supervised learning , select to maximize
      • often, better with small datasets
      • some approaches care about (speech recognition, statistical machine translation,...)
      • arguably, preferable with latent variables

HMMs, PCFGs (including the LA-PCFGs), ...

global discr vs local discr
Global Discr. vs Local Discr.
  • Local (distribs over small decsions) MEMMs, SVM decision classifiers in Nivre’s shift reduce parser
    • Pros:
      • no real decoding at training time (cheap learning)
      • complex features of can be integrated easily (about training! still need to decode at testing)
    • Cons:
      • mismatch btw test and train modes: rely on true features in training and on predicted ones in testing
      • label bias (cannot dump a unlikely transition if the number of outgoing states is not sufficiently large)
  • Global (distribs over the entire sequences) structperceptron, CRFs, M3Ns (model: MST parser)
    • Pros
      • Theoretically much cleaner and in practice works better
    • Cons
      • Decoding at training time (+ partition function for CRFs); but approximate learning methods exist
      • Learning can be very problematic if complex features of are used
  • Both models require decoding at testing. Decoding does not really depend on the training criteria but on the features of
specific learning criteria
Specific learning criteria
  • CRFs
    • Maximize
  • Perceptron
    • Ensure separability on the training set (with large margin in some variations – e.g., ALMA): rank correct structure above incorrect one
  • Max-Margin Markov Networks (M3Ns)
    • Separate training set with maximal margin (sensitively to the error)
    • For every labeled example
    • where is any structure, is some loss function (e.g., Hamming distance for sequence measuring how many labels do not match)
    • “Wrong sequences with small errors should be penalized less than with more errors”
    • SVM-Struct, Boosting, ....
decoding at training vs testing examples
Decoding at training vs testing: examples
  • Different combinations are possible ....
inference argmax
Inference (argmax)
  • Simple dependencies in y:
    • Viterbi to find the most likely sequence (or, Chi-Liu-Edmonds for MST)
    • Or, marginal decoding to find the most likely label for every “position”
  • Complex dependencies:
    • Beam or greedy search (or some smarter search methods)
    • Reformulate the inference problems as a integer linear program and use methods known in ILP
  • (We do not care here when the inference is used: either at training or testing, or at both)
latent variables vs explicit features
Latent Variables vs Explicit Features
  • Explicit features:
    • Pros:
      • Mostly convex optimization (no local minima)
      • Cheaper to learn
    • Cons:
      • Models is as good as the features are: extensive feature engineering needed
      • Non local dependencies in y are often necessary
  • Latent variable models:
    • Pros:
      • Learn how to propagate relevant information (learns complex features from simple ones)
      • Can learn a model with simple decompositions over extended y -- efficient decoding
      • Latent representation (e.g., extended parsing states or extended grammar) can potentially be useful in other tasks – multi-task learning
    • Cons:
      • Non-convex optimization – need to avoid local minima (tricky)
      • More expensive to train

Most of the model we considered: CRFs, MEMMs, etc

LA-PCFGs, ISBNs

last bits
Last bits
  • Term paper: due Mar 31 but send me ideas, outlines, draft well before the deadline (soon!)
  • Feedback on the content would be very much appreciated (as I am preparing a lecture class with a similar set of topics)
  • Thanks for participating!!!