# Technical notes: Syntax-aware Representation Learning With Pointer Networks

## TL;DR

This paper introduces a novel sequence-to-sequence dependency parsing model combining BiLSTM and Pointer Networks with logistic regression, showing promising initial results on the English Penn-treebank dataset.

## Contribution

It proposes a new dependency parsing approach using Pointer Networks with logistic regression, emphasizing the development of latent syntactic knowledge.

## Key findings

- Achieved 93.14% UAS on Penn-treebank without fine-tuning.
- Outperforms some existing baselines by 2-3%.
- Provides a promising baseline for future improvements.

## Abstract

This is a work-in-progress report, which aims to share preliminary results of a novel sequence-to-sequence schema for dependency parsing that relies on a combination of a BiLSTM and two Pointer Networks (Vinyals et al., 2015), in which the final softmax function has been replaced with the logistic regression. The two pointer networks co-operate to develop a latent syntactic knowledge, by learning the lexical properties of "selection" and the lexical properties of "selectability", respectively. At the moment and without fine-tuning, the parser implementation gets a UAS of 93.14% on the English Penn-treebank (Marcus et al., 1993) annotated with Stanford Dependencies: 2-3% under the SOTA but yet attractive as a baseline of the approach.