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.
Task-Adversarial Co-Generative Nets
In this paper, we propose Task-Adversarial co-Generative
Nets (TAGN) for learning from multiple tasks. It aims to
address the two fundamental issues of multi-task learning, i.e.,
domain shift and limited labeled data, in a principled way. To
this end, TAGN first learns the task-invariant representations
of features to bridge the domain shift among tasks. Based
on the task-invariant features, TAGN generates the plausible
examples for each task to tackle the data scarcity issue.
In TAGN, we leverage multiple game players to gradually
improve the quality of the co-generation of features and examples
by using an adversarial strategy. It simultaneously learns
the marginal distribution of task-invariant features across
different tasks and the joint distributions of examples with
labels for each task. The theoretical study shows the desired
results: at the equilibrium point of the multi-player game, the
feature extractor exactly produces the task-invariant features
for different tasks, while both the generator and the classifier
perfectly replicate the joint distribution for each task. The
experimental results on the benchmark data sets demonstrate
the effectiveness of the proposed approach.
- Publication Date:
- NSF-PAR ID:
- 10159296
- Journal Name:
- KDD
- Page Range or eLocation-ID:
- 1596 to 1604
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3292500.3330843
