Tuning SMT with a Large Number of Features via Online Feature Grouping

@inproceedings{Liu2013TuningSW,
  title={Tuning SMT with a Large Number of Features via Online Feature Grouping},
  author={Lemao Liu and Tiejun Zhao and Taro Watanabe and Eiichiro Sumita},
  booktitle={IJCNLP},
  year={2013}
}
In this paper, we consider the tuning of statistical machine translation (SMT) models employing a large number of features. We argue that existing tuning methods for these models suffer serious sparsity problems, in which features appearing in the tuning data may not appear in the testing data and thus those features may be over tuned in the tuning data. As a result, we face an over-fitting problem, which limits the generalization abilities of the learned models. Based on our analysis, we… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
3 Citations
17 References
Similar Papers

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…