  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.

Computation and Language;Machine Learning;Machine Learning