Robust Multilingual Part-of-Speech Tagging via Adversarial Training

Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. Yet, the specific effects of the robustness obtained from AT are still unclear in the context of natural language processing. In this paper, we propose and analyze a neural POS tagging model that exploits AT. In our experiments on the Penn Treebank WSJ corpus and the Universal Dependencies (UD) dataset (27 languages), we find that AT not only improves the overall tagging accuracy, but also 1) prevents over-fitting well in low resource languages and 2) boosts tagging accuracy for rare / unseen words. We also demonstrate that 3) the improved tagging performance by AT contributes to the downstream task of dependency parsing, and that 4) AT helps the model to learn cleaner word representations. 5) The proposed AT model is generally effective in different sequence labeling tasks. These positive results motivate further use of AT for natural language tasks.Comment: NAACL 201

Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form-function relationship in language, our "composed" word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish)

Towards robust cross-domain domain adaptation for part-of-speech tagging

Most systems in natural language processing experience a substantial loss in performance when the data that the system is tested with differs significantly from the data that the system has been trained on. Systems for part-of-speech (POS) tagging, for example, are typically trained on newspaper texts but are often applied to texts of other domains such as medical texts. Domain adaptation (DA) techniques seek to improve such systems so that they are able to achieve consistently good performance - independent of the domains at hand. We investigate the robustness of domain adaptation representations and methods across target domains using part-of-speech tagging as a case study. We find that there is no single representation and method that works equally well for all target domains. In particular, there are large differences between target domains that are more similar to the source domain and those that are less similar