Towards User-Driven Neural Machine Translation

TL;DR

This paper introduces a user-driven neural machine translation framework that captures user traits from historical inputs to produce personalized translations, addressing current limitations in modeling user behavior.

Contribution

The paper proposes a novel cache-based module and contrastive learning method for zero-shot user trait modeling in NMT, and introduces the first Chinese-English user behavior annotated corpus.

Findings

User-driven NMT generates personalized translations.

The framework effectively captures user traits from historical inputs.

Experimental results show improved translation personalization.

Abstract

A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior (e.g., historical inputs). However, current NMT systems marginally consider the user behavior due to: 1) the difficulty of modeling user portraits in zero-shot scenarios, and 2) the lack of user-behavior annotated parallel dataset. To fill this gap, we introduce a novel framework called user-driven NMT. Specifically, a cache-based module and a user-driven contrastive learning method are proposed to offer NMT the ability to capture potential user traits from their historical inputs under a zero-shot learning fashion. Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus. Experimental results confirm that the proposed user-driven NMT can generate user-specific translations.