RNN-based Alignment Model | Under the recurrence, the proposed model compactly encodes the entire history of previous alignments in the hidden layer configuration 3),. |
RNN-based Alignment Model | Therefore, the proposed model can find alignments by taking advantage of the long alignment history, while the FFNN-based model considers only the last alignment. |
Training | We evaluated the alignment performance of the proposed models with two tasks: Japanese-English word alignment with the Basic Travel Expression Corpus (BTEC) (Takezawa et a1., 2002) and French-English word alignment with the Hansard dataset (H ansards) from the 2003 NAACL shared task (Mihalcea and Pedersen, 2003). |
Training | In addition, Table 3 shows that these proposed models are comparable to IBM4au in NTCIR and FBIS even though the proposed models are trained from only a small part of the training data. |
Training | Our experiments have shown that the proposed model outperforms the FFNN-based model (Yang et al., 2013) for word alignment and machine translation, and that the agreement constraint improves alignment performance. |
Conclusion | The experimental results show that significant improvements have been achieved on various test data, meanwhile the translations are more cohesive and smooth, which together demonstrate the effectiveness of our proposed models . |
Experiments | To test the effectiveness of the proposed models , we have compared the translation quality of different integration strategies. |
Experiments | To further evaluate the effectiveness of the proposed models , we also conducted an experiment on a larger set of bilingual training data from the LDC corpus7 for translation model and transfer model. |
Experiments | The results in Table 4 further verify the effectiveness of our proposed models . |
Related Work | To the best of our knowledge, our work is the first attempt to exploit the source functional relationship to generate the target transitional expressions for grammatical cohesion, and we have successfully incorporated the proposed models into an SMT system with significant improvement of BLEU metrics. |
Conclusion | Focusing on the three financial crisis related datasets, the proposed model significantly outperform the standard linear regression method in statistics and strong discriminative support vector regression baselines. |
Conclusion | By varying the size of the training data and the dimensionality of the covariates, we have demonstrated that our proposed model is relatively robust across different parameter settings. |
Copula Models for Text Regression | Christensen (2005) shows that sorting and balanced binary trees can be used to calculate the correlation coefficients with complexity of 0(nlog Therefore, the computational complexity of MLE for the proposed model is O(n log |
Discussions | main questions we ask are: how is the proposed model different from standard text regres-siorflclassification models? |
Evaluation | “CharPos” stands for our proposed model which has been described in section 3. |
Evaluation | The results show that, while the differences between the baseline model and the proposed model in word segmentation accuracies are small, the proposed model achieves significant improvement in the experiment of joint segmentati- |
Evaluation | As the results show, despite the fact that the performance of our baseline model is relatively weak in the joint segmentation and POS tagging task, our proposed model achieves the second-best performance in both segmentation and joint tasks. |
Abstract | The experiments on a Chinese-to-English machine translation task reveal that the proposed model can bring positive segmentation effects to translation quality. |
Conclusion | The empirical results indicate that the proposed model can yield better segmentations for SMT. |
Introduction | Section 4 reports the experimental results of the proposed model for a Chinese-to-English MT task. |
Abstract | Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%. |
Conclusions and Future Work | Instead of employing labeled corpora for training, the proposed model only requires the identification of named entities, locations and time expressions. |
Conclusions and Future Work | Our proposed model has been evaluated on the FSD corpus. |