Search-based Structured Prediction (0907.0786v1)

Abstract: We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem.

• The paper introduces Searn, which converts structured prediction tasks into cost-sensitive binary classification problems.
• It demonstrates competitive performance on tasks such as handwriting recognition, NER, syntactic chunking, and document summarization.
• The approach’s flexibility in integrating any classifier and loss function broadens its applicability across domains like NLP, vision, and bioinformatics.

Overview of Search-based Structured Prediction

The discussed paper introduces "Searn", a novel algorithm designed to address complex structured prediction problems across various domains, including natural language processing, speech recognition, computational biology, and computer vision. The Searn algorithm is distinctive for its integration of search processes with learning methods, transforming intricate structured prediction tasks into manageable binary classification challenges. This transformation allows for the utilization of any binary classifier, circumventing the need for complex decomposition of loss and feature functions typical in other structured learning approaches.

Key Aspects of Searn

Searn operates by employing a meta-algorithm that integrates structured prediction with binary classification. The process involves:

• Core Representation: Converting structured prediction problems into sequences that can be easily handled by binary classifiers.
• Theoretical Guarantee: Searn provides a robust theoretical foundation whereby good performance in derived classification tasks ensures corresponding performance improvements in the overall structured prediction effort.
• Versatility and Flexibility: It is compatible with any loss function and feature set class, eliminating the constraints imposed by the need for feature and loss decomposition seen in traditional methods.

Algorithmic Implementation

The Searn algorithm is characterized by iterative optimization, where each iteration refines the policy by synthesizing search-based decision-making with learning from past iterations. The problem is viewed as a series of decision-making states, and the learning model incrementally improves by evaluating decisions' impacts based on structured input data.

1. Initial Policy: Utilizes an initial policy that optimally decodes the structured task based on existing training data. This policy is refined iteratively to enhance prediction performance.
2. Cost-sensitive Learning: Converts the structured prediction task into a cost-sensitive classification problem, which is then addressed using any binary classification algorithm.
3. State-space Exploration: Employs a strategic search method, adapting various search spaces to interpret complex output structures coherently.

Experimental Validation

Searn was tested on several sequence labeling tasks—including handwriting recognition, named entity recognition (NER) in Spanish, syntactic chunking, and joint chunking with part-of-speech (POS) tagging. Across these diverse applications:

• Structured Prediction Capability: The algorithm showed competitive or superior performance compared to other methods such as CRFs, M^3Ns, and MEMMs.
• Document Summarization: In a more complex scenario, the algorithm outperformed traditional extractive summarization methods, achieving state-of-the-art results on the DUC 2005 data set.

Implications and Future Directions

Searn represents a significant methodological advancement in structured prediction, offering a framework that leverages binary classification to manage complex prediction tasks. Its flexibility and robustness against high-dimensional and structured output spaces suggest significant applicability in a range of domains. Future research could dive into the optimization of training efficiency, particularly regarding the handling of noisy data and incorporating semi-supervised learning paradigms. Additionally, exploring its applications beyond language tasks, such as image analysis and bioinformatics, may yield further innovations in structured learning processes.