Paper: A Fast Re-scoring Strategy to Capture Long-Distance Dependencies

ACL ID D11-1103
Title A Fast Re-scoring Strategy to Capture Long-Distance Dependencies
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

A re-scoring strategy is proposed that makes it feasible to capture more long-distance de- pendencies in the natural language. Two pass strategies have become popular in a num- ber of recognition tasks such as ASR (au- tomatic speech recognition), MT (machine translation) and OCR (optical character recog- nition). The first pass typically applies a weak language model (n-grams) to a lattice and the second pass applies a stronger lan- guage model to N best lists. The stronger lan- guage model is intended to capture more long- distance dependencies. The proposed method uses RNN-LM (recurrent neural network lan- guage model), which is a long span LM, to re- score word lattices in the second pass. A hill climbing method (iterative decoding) is pro- posed to search over islands of confusability...