Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora
This addresses the challenge of effectively filtering noisy parallel corpora for NMT systems, though it is incremental as it builds on existing feature-based filtering methods.
The paper tackles the problem of noisy web-crawled corpora limiting Neural Machine Translation performance by learning weights for multiple sentence-level features optimized for translation improvement, resulting in beating strong baselines and hand-designed combinations for Estonian-English and exploring generalization to Maltese-English.
Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (NMT) systems across several language pairs. However, since these corpora are typically extremely noisy, their use is fairly limited. Current approaches to dealing with this problem mainly focus on filtering using heuristics or single features such as language model scores or bi-lingual similarity. This work presents an alternative approach which learns weights for multiple sentence-level features. These feature weights which are optimized directly for the task of improving translation performance, are used to score and filter sentences in the noisy corpora more effectively. We provide results of applying this technique to building NMT systems using the Paracrawl corpus for Estonian-English and show that it beats strong single feature baselines and hand designed combinations. Additionally, we analyze the sensitivity of this method to different types of noise and explore if the learned weights generalize to other language pairs using the Maltese-English Paracrawl corpus.