Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The accuracy of many natural language processing tasks can be improved by a reranking step, which involves selecting a single output from a list of candidate outputs generated by a baseline system.  propose a novel family of reranking algorithms based on learning separate low-dimensional embeddings of the task's input and output spaces. This embedding is learned in such a way that prediction becomes a low-dimensional nearest-neighbor search, which can be done computationally efficiently. A key quality of our approach is that feature engineering can be done separately on the input and output spaces; the relationship beten inputs and outputs is learned automatically. Experiments on part-of-speech tagging task in four languages show significant improvements over a baseline decoder and existing reranking approaches.
['Jagadeesh Jagarlamudi', 'Hal Daumé']
I am writing a research paper on a idea Low-Dimensional Discriminative Reranking
[{"from": "human", "value": "I am writing a research paper on a idea Low-Dimensional Discriminative Reranking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The accuracy of many natural language processing tasks can be improved by a reranking step, which involves selecting a single output from a list of candidate outputs generated by a baseline system.  propose a novel family of reranking algorithms based on learning separate low-dimensional embeddings of the task's input and output spaces. This embedding is learned in such a way that prediction becomes a low-dimensional nearest-neighbor search, which can be done computationally efficiently. A key quality of our approach is that feature engineering can be done separately on the input and output spaces; the relationship beten inputs and outputs is learned automatically. Experiments on part-of-speech tagging task in four languages show significant improvements over a baseline decoder and existing reranking approaches."}]