Fusion of Word and Letter Based Metrics for Automatic MT Evaluation
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2204-2209.
Abstract
With the progress in machine translation, it becomes more subtle to develop the evaluation metric capturing the systems' differences in comparison to the human translations. In contrast to the current efforts in leveraging more linguistic information to depict translation quality, this paper takes the thread of combining language independent features for a robust solution to MT evaluation metric. To compete with finer granularity of modeling brought by linguistic features, the proposed method augments the word level metrics by a letter based calculation. An empirical study is then conducted over WMT data to train the metrics by ranking SVM. The results reveal that the integration of current language independent metrics can generate well enough performance for a variety of languages. Time-split data validation is promising as a better training setting, though the greedy strategy also works well.
@inproceedings{ijcai2013p325,
title = {Fusion of Word and Letter Based Metrics for Automatic MT Evaluation},
author = {Yang, Muyun and Zhu, Junguo and Li, Sheng and Zhao, Tiejun},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2204--2209},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/325},
}
TY - CPAPER TI - Fusion of Word and Letter Based Metrics for Automatic MT Evaluation AU - Yang, Muyun AU - Zhu, Junguo AU - Li, Sheng AU - Zhao, Tiejun T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2204 EP - 2209 UR - https://ijcai.org/proceedings/2013/325 ER -Copied
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