We introduce a globally normalized transition-based neural network model that achieves
state-of-the-art part-of-speech tagging, dependency parsing and sentence compression …

We present an LSTM approach to deletion-based sentence compression where the task is to
translate a sentence into a sequence of zeros and ones, corresponding to token deletion …

We introduce a novel architecture for dependency parsing:\emph {stack-pointer
networks}(\textbf {\textsc {StackPtr}}). Combining pointer networks~\citep …

This paper presents a sequence to sequence (seq2seq) dependency parser by directly
predicting the relative position of head for each given word, which therefore results in a truly …

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers
a synchronous framework for deriving syntactic structures and semantic logical forms. In …

In this paper, we propose a neural network model for graph-based dependency parsing
which utilizes Bidirectional LSTM (BLSTM) to capture richer contextual information instead of …

We present structured perceptron training for neural network transition-based dependency
parsing. We learn the neural network representation using a gold corpus augmented by a …

We present a new method for semantic role labeling in which arguments and semantic roles
are jointly embedded in a shared vector space for a given predicate. These embeddings …

Dependency parsers are among the most crucial tools in natural language processing as
they have many important applications in downstream tasks such as information retrieval …

Conventional graph-based dependency parsers guarantee a tree structure both during
training and inference. Instead, we formalize dependency parsing as the problem of …