The massive amounts of web-mined parallel data contain large amounts of
noise. Semantic misalignment, as the primary source of the noise, poses a
challenge for training machine translation systems. In this paper, we first
study the impact of real-world hard-to-detect misalignment noise by proposing a
process to simulate the realistic misalignment controlled by semantic
similarity. After quantitatively analyzing the impact of simulated misalignment
on machine translation, we show the limited effectiveness of widely used
pre-filters to improve the translation performance, underscoring the necessity
of more fine-grained ways to handle data noise. By observing the increasing
reliability of the model's self-knowledge for distinguishing misaligned and
clean data at the token-level, we propose a self-correction approach which
leverages the model's prediction distribution to revise the training
supervision from the ground-truth data over training time. Through
comprehensive experiments, we show that our self-correction method not only
improves translation performance in the presence of simulated misalignment
noise but also proves effective for real-world noisy web-mined datasets across
eight translation tasks.

通过模拟实际语义不对齐问题并对其影响进行定量分析，本研究提出一种自我校正方法来处理数据噪声，并在机器翻译中取得了显著的性能提升。