Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A re-scoring strategy is proposed that makes it feasible to capture more long-distance dependencies in the natural language. Two pass strategies have become popular in a number of recognition tasks such as ASR (automatic speech recognition), MT (machine translation) and OCR (optical character recognition). The first pass typically applies a ak language model (n-grams) to a lattice and the second pass applies a stronger language model to N best lists. The stronger language model is intended to capture more long-distance dependencies. The proposed method uses RNN-LM (recurrent neural network language model), which is a long span LM, to re-score word lattices in the second pass. A hill climbing method (iterative decoding) is proposed to search over islands of confusability in the word lattice. An evaluation based on Broadcast News shows speedups of 20 over basic N best re-scoring, and word error rate reduction of 8% (relative) on a highly competitive setup.
['Anoop Deoras', 'Tomas Mikolov', 'Kenneth Church']
I am writing a research paper on a idea A Fast Re-scoring Strategy to Capture Long-Distance Dependencies
[{"from": "human", "value": "I am writing a research paper on a idea A Fast Re-scoring Strategy to Capture Long-Distance Dependencies"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A re-scoring strategy is proposed that makes it feasible to capture more long-distance dependencies in the natural language. Two pass strategies have become popular in a number of recognition tasks such as ASR (automatic speech recognition), MT (machine translation) and OCR (optical character recognition). The first pass typically applies a ak language model (n-grams) to a lattice and the second pass applies a stronger language model to N best lists. The stronger language model is intended to capture more long-distance dependencies. The proposed method uses RNN-LM (recurrent neural network language model), which is a long span LM, to re-score word lattices in the second pass. A hill climbing method (iterative decoding) is proposed to search over islands of confusability in the word lattice. An evaluation based on Broadcast News shows speedups of 20 over basic N best re-scoring, and word error rate reduction of 8% (relative) on a highly competitive setup."}]