变压器架构 在机器翻译中寻求改善域外通用化
Yiheng He1, Ruiyi Zhang1, Sai Ashish Somayajula1
1UC San Diego.
概括
这项研究引入了一种新的方法,可以自动找到在机器翻译 (MT) 任务中表现良好的变压器神经架构,即使是域外 (OOD) 数据. 该方法通过在架构搜索过程中合成OOD数据来增强概括性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 对于机器翻译 (MT) 的变压器神经架构的自动搜索正在获得吸引力.
- 现有的方法在域内情景中表现出色,但在域外 (OOD) 数据方面却失败,这在现实应用中很常见.
- 优化用于训练数据的变压器架构由于分布转移而与OOD测试数据发生斗争.
研究的目的:
- 提出一种多级优化方法,用于自动搜索具有强大的OOD概括能力的神经架构.
- 开发一种综合方法,同时合成近似的OOD MT数据并寻找最佳架构.
- 为了提高机器翻译在不同数据分布的现实场景中的准确性.
主要方法:
- 采用多级优化框架来搜索变压器架构.
- 在架构搜索过程中合成了近似的域外 (OOD) 机器翻译数据.
- 整合OD数据和架构搜索的合成被整合到一个端到端的过程中来评估和增强泛化.
主要成果:
- 拟议的方法在多个数据集中展示了强大的OOD泛化性能.
- 这种方法超越了现有的最先进的方法来处理域外机器翻译.
- 合成的OOD数据有效地有助于提高搜索架构的概括能力.
结论:
- 开发的方法提供了一个强大的解决方案,可以自动发现对OOD机器翻译很好地概括的变压器架构.
- 集成的OOD数据合成和架构搜索提供了有效的策略来应对MT的分布转移.
- 公开可用的代码有助于进一步研究和应用这些OOD-robust架构搜索技术.
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