Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts
In this paper we present a step-by-step approach to long-form text translation, drawing on established processes in translation studies. Instead of viewing machine translation as a single, monolithic task, we propose a framework that engages language models in a multi-turn interaction, encompassing pre-translation research, drafting, refining, and proofreading, resulting in progressively improved translations. Extensive automatic evaluations using Gemini 1.5 Pro across ten language pairs show that translating step-by-step yields large translation quality improvements over conventional zero-shot prompting approaches and earlier human-like baseline strategies, resulting in state-of-the-art results on WMT2024.
Code (0)
등록된 구현이 없습니다.
Tasks
FormMachine TranslationTranslationSimilar Papers 제목 키워드 기반
Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation
Large Language Models (LLMs) demonstrate strong reasoning capabilities for many tasks, often by explicitly decomposing the task via Chain-of-Thought (CoT) reasoning. Recent work on LLM-based translation designs hand-craf…
TranslationTranslating Translationese: A Two-Step Approach to Unsupervised Machine Translation
Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation i…
DecoderMachine TranslationSentenceTranslation+2Two-Step MT: Predicting Target Morphology
This paper describes a two-step machine translation system that addresses the issue of translating into a morphologically rich language (English to Czech), by performing separately the translation and the generation of t…
Machine TranslationTranslationVocal Bursts Valence PredictionLLM-Aided Compilation for Tensor Accelerators
Hardware accelerators, in particular accelerators for tensor processing, have many potential application domains. However, they currently lack the software infrastructure to support the majority of domains outside of dee…
An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages
Byte-Pair Encoding (BPE) (Sennrich et al., 2016) has become a standard pre-processing step when building neural machine translation systems. However, it is not clear whether this is an optimal strategy in all settings. W…
Language ModelingLanguage ModellingMachine TranslationTranslation