Paper: Solving The Problem Of Cascading Errors: Approximate Bayesian Inference For Linguistic Annotation Pipelines

ACL ID W06-1673
Title Solving The Problem Of Cascading Errors: Approximate Bayesian Inference For Linguistic Annotation Pipelines
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

The end-to-end performance of natural language processing systems for com- pound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline archi- tecture, which causes errors to propagate and compound at each stage. We present a novel architecture, which models these pipelines as Bayesian networks, with each low level task corresponding to a variable in the network, and then we perform ap- proximate inference to find the best la- beling. Our approach is extremely sim- ple to apply but gains the benefits of sam- pling the entire distribution over labels at each stage in the pipeline. We apply our method to two tasks – semantic role la- beling and recognizing textual entailment – and achieve useful performance gains from the superior p...