In the deep learning (DL) era, parsing models are extremely simplified with little hurt on
performance, thanks to the remarkable capability of multi-layer BiLSTMs in context …

In this paper, we propose second-order graph-based neural dependency parsing using
message passing and end-to-end neural networks. We empirically show that our …

Higher-order methods for dependency parsing can partially but not fully address the issue
that edges in dependency trees should be constructed at the text span/subtree level rather …

We present a simple and unified approach for both continuous and discontinuous
constituency parsing via autoregressive span selection. Constituency parsing aims to …

We investigate the ability of transformer models to approximate the CKY algorithm, using
them to directly predict a sentence's parse and thus avoid the CKY algorithm's cubic …

Memory-based learning can be characterized as a lazy learning method in machine
learning terminology because it delays the processing of input by storing the input until …

This work visits the topic of jointly parsing constituency and dependency trees, ie, to produce
compatible constituency and dependency trees simultaneously for input sentences, which is …

This work revisits the topic of jointly parsing constituency and dependency trees, ie, to
produce compatible constituency and dependency trees simultaneously for input sentences …

Mannual annotation for dependency parsing is both labourious and time costly, resulting in
the difficulty to learn practical dependency parsers for many languages due to the lack of …

Source-Free Transductive Transfer Learning for Structured Prediction Page 1 Source-Free
Transductive Transfer Learning for Structured Prediction by Kemal Maulana Kurniawan …