# Selective Attention for Context-aware Neural Machine Translation

## TL;DR

This paper introduces a scalable hierarchical attention mechanism for document-level neural machine translation, significantly improving translation quality by selectively focusing on relevant context sentences and words.

## Contribution

It proposes a novel top-down sparse attention approach for context-aware NMT that effectively scales to full documents and outperforms existing methods.

## Key findings

- Outperforms context-agnostic baselines in translation quality
- Surpasses existing context-aware models in most evaluation cases
- Effective in English-German document translation tasks

## Abstract

Despite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document. Recent works in context-aware NMT consider only a few previous sentences as context and may not scale to entire documents. To this end, we propose a novel and scalable top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context and then attends to key words in those sentences. We also propose single-level attention approaches based on sentence or word-level information in the context. The document-level context representation, produced from these attention modules, is integrated into the encoder or decoder of the Transformer model depending on whether we use monolingual or bilingual context. Our experiments and evaluation on English-German datasets in different document MT settings show that our selective attention approach not only significantly outperforms context-agnostic baselines but also surpasses context-aware baselines in most cases.