Abstract
Fully machine translation scarcely guarantees error-free results. Humans perform post-editing on machine generated translations to correct errors in the scenario of computer aided translation. In favor of expediting the post-editing process, recent works have investigated machine translation in an interactive mode, where machines can automatically refine the rest of translations constrained on human's edits. In this paper, we utilize the parameterized objective function of neural machine translation and propose an easy constrained decoding algorithm to improve the translation quality without additional training. We demonstrate its capability and time efficiency on a benchmark dataset, WeTS, where it conditions on humans' guidelines by selecting spans with potential errors. In the experimental results, our algorithm is significantly superior to state-of-the-art lexically constrained decoding method by an increase of 10.37 BLEU in translation quality and a decrease of 63.4% in time cost on average. It even outperforms the benchmark systems trained with a large amount of annotated data on WeTS in English-German and German-English.
Abstract (translated)
URL
https://arxiv.org/abs/2211.07093
