论文标题
多通道生成语言模型：学习渠道内部和跨渠道内的所有可能因素化
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels
论文作者
论文摘要
通道对应于基本含义的观点或转换。一对英语和法语的平行句子表达了相同的基本含义，但通过与他们的语言相对应的两个单独的频道。在这项工作中，我们介绍了多通道生成语言模型（MGLM）。 MGLM是通道上的生成联合分布模型。 MGLM在所有渠道内部和所有可能的因素上都边缘化。 MGLM赋予灵活推断，包括无条件产生，条件生成（观察到1个通道和其他通道），并部分观察到产生（其中不完整的观测值分布在所有通道中）。我们尝试使用包含英语，法语，捷克和德语的多30k数据集。我们证明了无条件，条件和部分条件产生的实验。我们提供从生成关节分布无条件采样的定性样品。我们还定量分析了质量多样性权衡取舍，并发现MGLM的表现优于传统的双语判别模型。
A channel corresponds to a viewpoint or transformation of an underlying meaning. A pair of parallel sentences in English and French express the same underlying meaning, but through two separate channels corresponding to their languages. In this work, we present the Multichannel Generative Language Model (MGLM). MGLM is a generative joint distribution model over channels. MGLM marginalizes over all possible factorizations within and across all channels. MGLM endows flexible inference, including unconditional generation, conditional generation (where 1 channel is observed and other channels are generated), and partially observed generation (where incomplete observations are spread across all the channels). We experiment with the Multi30K dataset containing English, French, Czech, and German. We demonstrate experiments with unconditional, conditional, and partially conditional generation. We provide qualitative samples sampled unconditionally from the generative joint distribution. We also quantitatively analyze the quality-diversity trade-offs and find MGLM outperforms traditional bilingual discriminative models.
