• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>China National Conference on Computational Linguistics >A Mixed Learning Objective for Neural Machine Translation
【24h】

A Mixed Learning Objective for Neural Machine Translation

机译：神经机翻译的混合学习目标

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Evaluation discrepancy and overcorrection phenomenon are two common problems in neural machine translation (NMT). NMT models are generally trained with word-level learning objective, but evaluated by sentence-level metrics. Moreover, the cross-entropy loss function discourages model to generate synonymous predictions and overcor-rect them to ground truth words. To address these two drawbacks, we adopt multi-task learning and propose a mixed learning objective (MLO) which combines the strength of word-level and sentence-level evaluation without modifying model structure. At word-level, it calculates semantic similarity between predicted and ground truth words. At sentence-level, it computes probabilistic n-gram matching scores of generated translations. We also combine a loss-sensitive scheduled sampling decoding strategy with MLO to explore its extensibility. Experimental results on IWSLT 2016 German-English and WMT 2019 English-Chinese datasets demonstrate that our methodology can significantly promote translation quality. The ablation study shows that both word-level and sentence-level learning objectives can improve BLEU scores. Furthermore, MLO is consistent with state-of-the-art scheduled sampling methods and can achieve further promotion.
机译：评估差异和过度矫正现象是神经机翻译（NMT）中的两个常见问题。 NMT模型通常受到单词级学习目标的培训，而是由句子级度量评估。此外，跨熵损失函数不鼓励模型，以产生同义预测和过度矩阵对地面的话语。为了解决这两个缺点，我们采用多任务学习，并提出混合学习目标（MLO），其结合了字级和句子级评估的强度而不修改模型结构。在Word级，它计算预测和地面真理词之间的语义相似性。在句子级，它计算了所生成的翻译的概率N-GRAM匹配。我们还将损失敏感的计划采样解码策略与MLO结合起来探索其可扩展性。 IWSLT的实验结果2016年德语 - 英语和WMT 2019英语 - 中文数据集表明，我们的方法可以显着促进翻译质量。消融研究表明，单词级和句子级学习目标可以改善BLEU分数。此外，MLO与最先进的预定采样方法一致，可以实现进一步的促销。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号