LowResource Text Classification Using DomainAdversarial Learning Deep learning techniques have recently shown to be successful in many natural language processing tasks forming stateoftheart systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domainadversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural network in lowresource and zeroresource settings in new target domains or languages. In case of new languages, we show that monolingual wordvectors can be directly used for training without prealignment. Their projection into a common space can be learnt adhoc at training time reaching the final performance of pretrained multilingual wordvectors.

DomainAdversarial Graph Neural Networks for Text Classification Text classification, in crossdomain setting, is a challenging task. On the one hand, data from other domains are often useful to improve the learning on the target domain; on the other hand, domain variance and hierarchical structure of documents from words, key phrases, sentences, paragraphs, etc. make it difficult to align domains for effective learning. To date, existing crossdomain text classification methods mainly strive to minimize feature distribution differences between domains, and they typically suffer from three major limitations xe2x80x94 (1) difficult to capture semantics in nonconsecutive phrases and longdistance word dependency because of treating texts as word sequences, (2) neglect of hierarchical coarsegrained structures of document for feature learning, and (3) narrow focus of the domains at instance levels, without using domains as supervisions to improve text classification. This paper proposes an endtoend, domainadversarial graph neural networks (DAGNN), for crossdomain text classification. Our motivation is to model documents as graphs and use a domainadversarial training principle to lean features from each graph (as well as learning the separation of domains) for effective text classification. At the instance level, DAGNN uses a graph to model each document, so that it can capture nonconsecutive and longdistance semantics. At the feature level, DAGNN uses graphs from different domains to jointly train hierarchical graph neural networks in order to learn good features. At the learning level, DAGNN proposes a domainadversarial principle such that the learned features not only optimally classify documents but also separates domains. Experiments on benchmark datasets demonstrate the effectiveness of our method in crossdomain classification tasks.