Recently, my colleague and Ph.D.-candidate Daniel Griesshaber published our joined work with Professor Ngoc Thang Vu at the 6th Conference on Statistical Language and Speech Processing (SLSP) in Brussels. Here is the paper’s abstract:
Deep learning techniques have recently shown to be successful in many natural language processing tasks forming state-of-the-art systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domain-adversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural network in low-resource and zero-resource settings in new target domains or languages. In the case of new languages, we show that monolingual word-vectors can be directly used for training without pre-alignment. Their projection into a common space can be learnt ad-hoc at training time reaching the final performance of pretrained multilingual word-vectors.