Iterative Refinement for Machine Translation
Existing machine translation decoding algorithms generate translations in a strictly monotonic fashion and never revisit previous decisions. As a result, earlier mistakes cannot be corrected at a later stage. In this paper, we present a translation scheme that starts from an initial guess and then makes iterative improvements that may revisit previous decisions. We parameterize our model as a convolutional neural network that predicts discrete substitutions to an existing translation based on an attention mechanism over both the source sentence as well as the current translation output. By making less than one modification per sentence, we improve the output of a phrase-based translation system by up to 0.4 BLEU on WMT15 German-English translation.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationSentenceTranslationSimilar Papers 제목 키워드 기반
Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation
We propose an efficient inference procedure for non-autoregressive machine translation that iteratively refines translation purely in the continuous space. Given a continuous latent variable model for machine translation…
de-enMachine TranslationSentenceTranslationFlexible Non-Autoregressive Neural Machine Translation via Repositioning Edit Operations
We introduce an iterative text refinement model to reduce the decoding space of non-autoregressive models by disentangling the token prediction and relative position prediction. We show that our model achieves promising …
Machine TranslationPositionPredictionTranslationWhat Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation
Iterative self-refinement is a simple inference-time strategy for machine translation: an LLM revises its own translation over multiple inference-time passes. Yet document-scale refinement remains poorly understood: 1) w…
Machine TranslationDeterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is general…
Caption GenerationDenoisingMachine TranslationTranslationLearning to Rewrite for Non-Autoregressive Neural Machine Translation
Non-autoregressive neural machine translation, which decomposes the dependence on previous target tokens from the inputs of the decoder, has achieved impressive inference speedup but at the cost of inferior accuracy. Pre…
Decoderde-enMachine TranslationTranslation