Exploring the Use of Attention within an Neural Machine Translation Decoder States to Translate Idioms

TL;DR

This paper investigates incorporating attention mechanisms within neural machine translation decoders to improve idiom translation, leveraging memory-augmented models to address long-distance dependencies in idiomatic language.

Contribution

It introduces a novel approach of using attention within NMT decoder states specifically for translating idioms, combining memory-augmented techniques with neural translation models.

Findings

Improved translation accuracy for idiomatic expressions

Enhanced handling of long-distance dependencies in translation

Demonstrated effectiveness of memory-augmented attention mechanisms

Abstract

Idioms pose problems to almost all Machine Translation systems. This type of language is very frequent in day-to-day language use and cannot be simply ignored. The recent interest in memory augmented models in the field of Language Modelling has aided the systems to achieve good results by bridging long-distance dependencies. In this paper we explore the use of such techniques into a Neural Machine Translation system to help in translation of idiomatic language.