Large-scale annotated corpora are a prerequisite to developing high-performance NLP systems. Such corpora are expensive to produce, limited in size, often demanding linguistic expertise. In this paper we use text rewriting as a means of increasing the amount of labeled data available for model training. Our method uses automatically extracted rewrite rules from comparable corpora and bitexts to generate multiple versions of sentences annotated with gold standard labels. We apply this idea to semantic role labeling and show that a model trained on rewritten data outperforms the state of the art on the CoNLL-2009 benchmark dataset.

We present a new approach for unsupervised semantic role labeling that leverages distributed representations.We induce embeddings to represent a predicate, its arguments and their complex interdependence.Argument embeddings are learned from surrounding contexts involving the predicate and neighboring arguments, while predicate embeddings are learned from argument contexts.The induced representations are clustered into roles using a linear programming formulation of hierarchical clustering, where we can model task-specific knowledge.Experiments show improved performance over previous unsupervised semantic role labeling approaches and other distributed word representation models.

State-of-the-art semantic role labelling systems require large annotated corpora to achieve full performance.Unfortunately, such corpora are expensive to produce and often do not generalize well across domains.Even in domain, errors are often made where syntactic information does not provide sufficient cues.In this paper, we mitigate both of these problems by employing distributional word representations gathered from unlabelled data.While straight-forward word representations of predicates and arguments improve performance, we show that further gains are achieved by composing representations that model the interaction between predicate and argument, and capture full argument spans.

Large-scale annotated corpora are a prerequisite to developing high-performance NLP systems. Such corpora are expensive to produce, limited in size, often demanding linguistic expertise. In this paper we use text rewriting as a means of increasing the amount of labeled data available for model training. Our method uses automatically extracted rewrite rules from comparable corpora and bitexts to generate multiple versions of sentences annotated with gold standard labels. We apply this idea to semantic role labeling and show that a model trained on rewritten data outperforms the state of the art on the CoNLL-2009 benchmark dataset.

Text simplification aims to rewrite text into simpler versions and thus make information accessible to a broader audience (e.g., non-native speakers, children, and individuals with language impairments). In this paper, we propose a model that simplifies documents automatically while selecting their most important content and rewriting them in a simpler style. We learn content selection rules from same-topic Wikipedia articles written in the main encyclopedia and its Simple English variant. We also use the revision histories of Simple Wikipedia articles to learn a quasi-synchronous grammar of simplification rewrite rules. Based on an integer linear programming formulation, we develop a joint model where preferences based on content and style are optimized simultaneously. Experiments on simplifying main Wikipedia articles show that our method significantly reduces the reading difficulty, while still capturing the important content.