Daume et al, 2006

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Duame, H., Langford, J., and Marcu, D. 2006. SEARN in Practice. Unpublished Manuscript

Online version

SEARN in Practice


This unpublished manuscript describes how SEARN can be used for three Natural Language Processing related tasks: Sequence Labeling, Parsing, and Machine Translation

The key points of the paper are:

  • Authors state that SEARN is efficient, widely applicable, theoretically justified, and simple.
  • SEARN looks at problems a search problems, and learns classifiers that walk through the search space in a good way.
  • Authors looked at 3 sample problems: Sequence Labeling, Parsing, and Machine Translation
  • Efficacy of SEARN hinges on ability to compute an optimal/near-optimal policy. When an optimal policy is not available, authors suggest performing explicit search as an approximation. For segmentaiton and parsing, optimal policy is closed form; for summarization and machine translation, the optimal policy is not available.

Example SEARN Usage

Sequence Labeling


  • Task is to produce a label sequence from an input sequence.
  • Search framed as left-to-right greedy search.
  • Loss function: Hamming loss
  • Optimal Policy:


NP Chunking

  • Chunking is a joint segmentation and labeling problem.
  • Loss function: F1 measure
  • Optimal Policy:



  • Looked at dependency parsing with a shift-reduce framework.
  • Loss funtion: Hamming loss over dependencies.
  • Decisions: shift/reduce
  • Optimal Policy:


Machine Translation

  • Framed task as a left-to-right translation problem.
  • Search space over prefixes of translations.
  • Actions are adding a word (or phrase to end of existing translation.
  • Loss function: 1 - BLEU or 1 - NIST
  • Optimal policy: given set of reference translations R, English translation prefix e_1, ... e_i-1, what word (or phrase) should be produced next / are we finished.

Related papers

  • Search-based Structured Prediction: This is the journal version of the paper that introduces the SEARN algorithm - Daume_et_al,_ML_2009.