Modeling the parser state is key to good performance in transition-based parsing. Recurrent
Neural Networks considerably improved the performance of transition-based systems by …

Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained
sequence-to-sequence Transformer models has recently led to large improvements on AMR …

We define a mapping from transition-based parsing algorithms that read sentences from left
to right to sequence labeling encodings of syntactic trees. This not only establishes a …

This paper introduces an effective method for improving dependency parsing which is based
on a graph embedding model. The model helps extract local and global connectivity …

Sequence-to-sequence constituent parsing requires a linearization to represent trees as
sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce …

In order to achieve deep natural language understanding, syntactic constituent parsing is a
vital step, highly demanded by many artificial intelligence systems to process both text and …

Sequence-to-sequence constituency parsing casts the tree structured prediction problem as
a general sequential problem by top-down tree linearization, and thus it is very easy to train …

We introduce an evaluation system designed to compute PARSEVAL measures, offering a
viable alternative to\texttt {evalb} commonly used for constituency parsing evaluation. The …

In recent years, Video Object Segmentation (VOS) has emerged as a complementary
method to Video Object Tracking (VOT). VOS focuses on classifying all the pixels around the …

We propose two fast neural combinatory models for constituency parsing: binary and multi-
branching. Our models decompose the bottom-up parsing process into 1) classification of …