Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a theoretical analysis of online parameter tuning in statistical machine translation (SMT) from a coactive learning view. This perspective allows us to give regret and generalization bounds for latent perceptron algorithms that are common in SMT, but fall outside of the standard convex optimization scenario. Coactive learning also introduces the concept of ak feedback, which  apply in a proofof-concept experiment to SMT, showing that learning from feedback that consists of slight improvements over predictions leads to convergence in regret and translation error rate. This suggests that coactive learning might be a viable framework for interactive machine translation. Furthermore,  find that surrogate translations replacing references that are unreachable in the decoder search space can be interpreted as ak feedback and lead to convergence in learning, if they admit an underlying linear model.
['Artem Sokolov', 'Stefan Riezler', 'Shay B. Cohen']
I am writing a research paper on a idea A Coactive Learning View of Online Structured Prediction in Statistical Machine Translation
[{"from": "human", "value": "I am writing a research paper on a idea A Coactive Learning View of Online Structured Prediction in Statistical Machine Translation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a theoretical analysis of online parameter tuning in statistical machine translation (SMT) from a coactive learning view. This perspective allows us to give regret and generalization bounds for latent perceptron algorithms that are common in SMT, but fall outside of the standard convex optimization scenario. Coactive learning also introduces the concept of ak feedback, which  apply in a proofof-concept experiment to SMT, showing that learning from feedback that consists of slight improvements over predictions leads to convergence in regret and translation error rate. This suggests that coactive learning might be a viable framework for interactive machine translation. Furthermore,  find that surrogate translations replacing references that are unreachable in the decoder search space can be interpreted as ak feedback and lead to convergence in learning, if they admit an underlying linear model."}]