Many intelligent systems in business, government or academy process natural language as
an input during inference or they might even communicate with users in natural language …

Semantic role labeling (SRL) is crucial to natural language understanding as it identifies the
predicate-argument structure in text with semantic labels. Unfortunately, resources required …

When Part-of-Speech annotated data is scarce, eg for under-resourced languages, one can
turn to cross-lingual transfer and crawled dictionaries to collect partially supervised data. We …

Cross-lingual information extraction is the task of distilling facts from foreign language (eg
Chinese text) into representations in another language that is preferred by the user (eg …

We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and
generates sentences with Semantic Role Labeling annotations in a resource-poor target …

We describe a transfer method based on annotation projection to develop a dependency-
based semantic role labeling system for languages for which no supervised linguistic …

Natural language processing (NLP) applications such as named entity recognition (NER) for
low-resource corpora do not benefit from recent advances in the development of large …

Our paper addresses the problem of annotation projection for semantic role labeling for
resource-poor languages using supervised annotations from a resource-rich language …

Semantic role labeling (SRL) is the task of identifying and labeling predicate-argument
structures in sentences with semantic frame and role labels. A known challenge in SRL is …

Semantic role labeling (SRL) is a central natural language processing (NLP) task aiming to
understand the semantic roles within texts, facilitating a wide range of downstream …