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

• URL: http://arxiv.org/abs/2407.02208v2
• Date: Fri, 07 Feb 2025 15:03:38 GMT
• Title: How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation
• Authors: Yan Meng, Di Wu, Christof Monz,
• Abstract summary: In this paper, we introduce a process for simulating misalignment controlled by semantic similarity. We quantitatively analyze its impact on machine translation and demonstrate the limited effectiveness of widely used pre-filters for noise detection. We propose self-correction, an approach that gradually increases trust in the model's self-knowledge to correct the training supervision.
• Score: 10.739338438716965
• License:
• 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 introduce a process for simulating misalignment controlled by semantic similarity, which closely resembles misaligned sentences in real-world web-crawled corpora. Under our simulated misalignment noise settings, we quantitatively analyze its impact on machine translation and demonstrate the limited effectiveness of widely used pre-filters for noise detection. This underscores the necessity of more fine-grained ways to handle hard-to-detect misalignment noise. With an observation of the increasing reliability of the model's self-knowledge for distinguishing misaligned and clean data at the token level, we propose self-correction, an approach that gradually increases trust in the model's self-knowledge to correct the training supervision. Comprehensive experiments show that our method significantly improves translation performance both in the presence of simulated misalignment noise and when applied to real-world, noisy web-mined datasets, across a range of translation tasks.

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