Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful
for various NLP sequence modeling tasks, but little is known about their reliance to input …

We train one multilingual model for dependency parsing and use it to parse sentences in
several languages. The parsing model uses (i) multilingual word clusters and …

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 …

We propose a novel approach to cross-lingual part-of-speech tagging and dependency
parsing for truly low-resource languages. Our annotation projection-based approach yields …

This task combines the labeling of multiword expressions and supersenses (coarse-grained
classes) in an explicit, yet broad-coverage paradigm for lexical semantics. Nine systems …

Morphologically rich languages often lack the annotated linguistic resources required to
develop accurate natural language processing tools. We propose models suitable for …

A recent study by Plank et al.(2016) found that LSTM-based PoS taggers considerably
improve over the current state-of-the-art when evaluated on the corpora of the Universal …

We introduce a novel framework for delexicalized dependency parsing in a new language.
We show that useful features of the target language can be extracted automatically from an …

In this paper we propose and carefully evaluate a sequence labeling framework which
solely utilizes sparse indicator features derived from dense distributed word representations …

We address the challenge of cross-lingual POS tagger evaluation in absence of manually
annotated test data. We put forth and evaluate two dictionary-based metrics. On the tasks of …