統計的機械翻訳のためのマージン最大化学習 : 機械翻訳精度向上に向けて
スポンサーリンク
概要
- 論文の詳細を見る
Minimum error rate training (MERT) has been a widely used learning method for statistical machine translation to estimate the feature function weights of a linear model. MERT has an advantage to incorpolate an automatic translation evaluation metrics as BLEU scores to its objective function. Weight vector can directly be optimized with Line search algorithm using error surface on a given set of candidate translations. It efficiently searches the best parameter resulting the highest BLEU scores. In this paper, we presented a new training algorithm for statisitcal machine translation, inspired by MERT and Structural Support Vector Machines. We performed MERT optimization by maximizing the margin between the oracle and incorrect translations under the L2-norm prior. Our experimental results on Japanese-English speech translation task showed that BLEU scores obtained by our proposed method were much better than those obtained by MERT. We achieved the best improvement of BLEU about +3.0 over standard MERT.
論文 | ランダム
- 情動的要因がマンガを利用した学習に与える影響--教材評価を中心に (平成21年度博士学課程生研究支援プログラム研究成課報告書)
- 東北地方における北方系細石刃石器群の波及と展開
- 古代の「側壁抉込土坑」について--宮城県内の調査事例から
- 宮城県の旧石器遺跡の現状
- 中国華北地区の旧石器見学旅行記