  Many text classification tasks are known to be highly domain-dependent.
Unfortunately, the availability of training data can vary drastically across
domains. Worse still, for some domains there may not be any annotated data at
all. In this work, we propose a multinomial adversarial network (MAN) to tackle
the text classification problem in this real-world multidomain setting (MDTC).
We provide theoretical justifications for the MAN framework, proving that
different instances of MANs are essentially minimizers of various f-divergence
metrics (Ali and Silvey, 1966) among multiple probability distributions. MANs
are thus a theoretically sound generalization of traditional adversarial
networks that discriminate over two distributions. More specifically, for the
MDTC task, MAN learns features that are invariant across multiple domains by
resorting to its ability to reduce the divergence among the feature
distributions of each domain. We present experimental results showing that MANs
significantly outperform the prior art on the MDTC task. We also show that MANs
achieve state-of-the-art performance for domains with no labeled data.
