Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Sequence model learning algorithms typically maximize log-likelihood minus the norm of the model (or minimize Hamming loss + norm). In cross-lingual part-of-speech (POS) tagging, our target language training data consists of sequences of sentences with word-by-word labels projected from translations in $k$ languages for which  have labeled data, via word alignments. Our training data is therefore very noisy, and if Rademacher complexity is high, learning algorithms are prone to overfit. Norm-based regularization assumes a constant width and zero mean prior.  instead propose to use the $k$ source language models to estimate the parameters of a Gaussian prior for learning new POS taggers. This leads to significantly better performance in multi-source transfer set-ups.  also present a drop-out version that injects (empirical) Gaussian noise during online learning. Finally,  note that using empirical Gaussian priors leads to much lor Rademacher complexity, and is superior to optimally ighted model interpolation.
['Anders Søgaard']
I am writing a research paper on a idea Empirical Gaussian priors for cross-lingual transfer learning
[{"from": "human", "value": "I am writing a research paper on a idea Empirical Gaussian priors for cross-lingual transfer learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Sequence model learning algorithms typically maximize log-likelihood minus the norm of the model (or minimize Hamming loss + norm). In cross-lingual part-of-speech (POS) tagging, our target language training data consists of sequences of sentences with word-by-word labels projected from translations in $k$ languages for which  have labeled data, via word alignments. Our training data is therefore very noisy, and if Rademacher complexity is high, learning algorithms are prone to overfit. Norm-based regularization assumes a constant width and zero mean prior.  instead propose to use the $k$ source language models to estimate the parameters of a Gaussian prior for learning new POS taggers. This leads to significantly better performance in multi-source transfer set-ups.  also present a drop-out version that injects (empirical) Gaussian noise during online learning. Finally,  note that using empirical Gaussian priors leads to much lor Rademacher complexity, and is superior to optimally ighted model interpolation."}]