# BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings

arXiv: https://arxiv.org/abs/1605.07874 · HTML: https://mlsanity.com/paper/1605.07874/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 3 of 3)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval](https://mlsanity.com/paper/2411.11875/) (2024)
- [Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation](https://mlsanity.com/paper/2305.16599/) (2023)

## Abstract (arXiv)

In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate clues and sourcetarget interactions at multiple levels of granularity into bilingual phrase representations. We employ recursive autoencoders to generate tree structures of phrases with embeddings at different levels of granularity (e.g., words, sub-phrases and phrases). Over these embeddings on the source and target side, we introduce a bidimensional attention network to learn their interactions encoded in a bidimensional attention matrix, from which we extract two soft attention weight distributions simultaneously. These weight distributions enable BattRAE to generate compositive phrase representations via convolution. Based on the learned phrase representations, we further use a bilinear neural model, trained via a max-margin method, to measure bilingual semantic similarity. To evaluate the effectiveness of BattRAE, we incorporate this semantic similarity as an additional feature into a state-of-the-art SMT system. Extensive experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.63 BLEU points on average over the baseline.