How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise on Machine Translation

• URL: http://arxiv.org/abs/2407.02208v1
• Date: Tue, 2 Jul 2024 12:15:15 GMT
• Title: How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise on Machine Translation
• Authors: Yan Meng, Di Wu, Christof Monz,
• Abstract summary: We study the impact of real-world hard-to-detect misalignment noise on machine translation. 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. Our method proves effective for real-world noisy web-mined datasets across eight translation tasks.
• Score: 10.739338438716965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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