零NLG:对准和自动编码领域为零射击多模式和多语言自然语言生成
IEEE transactions on pattern analysis and machine intelligence
|February 29, 2024
概括
零NLG是一种新的自然语言生成 (NLG) 零射击学习框架,绕过了标记数据的需求. 这种方法有效地处理各种任务,如图像标题和机器翻译跨多种语言.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 监督自然语言生成 (NLG) 方法需要广泛的标记数据-文本对.
- 非英语语言和特定场景的数据稀缺性阻碍了监督的NLG模型开发.
- 收集和标记数据是一个昂贵和耗时的过程.
研究的目的:
- 引入ZeroNLG,一个统一的自然语言生成 (NLG) 零射击学习框架.
- 为了使NLG任务能够跨越不同的模式 (图像,视频,文本) 和语言 (英语,中文,德语,法语),而无需标记下游数据.
- 为了减少对昂贵的标记数据集的依赖,用于训练NLG模型.
主要方法:
- 零NLG将各种数据领域投射到一个共享的潜空间中.
- 它在这个共享空间内的各个域中对应的坐标进行对齐.
- 一个无监督的多语言自动编码器被用于通过重建生成文本.
主要成果:
- 零NLG成功地执行零拍摄图像标题,视频标题和神经机器翻译.
- 该框架在英语,中文,德语和法语中有效运行.
- 在12个NLG任务上进行了广泛的实验,证明了高质量,可信的输出,优于现有的零射击方法.
结论:
- 对于数据稀缺的NLG场景,ZeroNLG提供了一个有效的解决方案.
- 统一的框架在多个任务和语言中展示了多功能性.
- 这种方法在自然语言生成中显著推进了零射击学习.
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