# Towards Bidirectional Hierarchical Representations for Attention-Based Neural Machine Translation

arXiv: https://arxiv.org/abs/1707.05114 · HTML: https://mlsanity.com/paper/1707.05114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization](https://mlsanity.com/paper/2110.10834/) (2021)

## Abstract (arXiv)

This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder. To maximize the predictive likelihood of target words, a weighted variant of an attention mechanism is used to balance the attentive information between lexical and phrase vectors. Using a tree-based rare word encoding, the proposed model is extended to sub-word level to alleviate the out-of-vocabulary (OOV) problem. Empirical results reveal that the proposed model significantly outperforms sequence-to-sequence attention-based and tree-based neural translation models in English-Chinese translation tasks.