Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation
This work addresses a bottleneck in machine translation for NLP practitioners by improving ranking accuracy, though it is incremental as it builds on existing pairwise methods.
The authors tackled the problem of ranking hypotheses in statistical machine translation by proposing a listwise learning framework that models the entire translation list's ordering, along with top-rank enhanced loss functions to prioritize higher positions. Experiments on a large-scale Chinese-English translation task showed significant improvements in translation quality.
Pairwise ranking methods are the basis of many widely used discriminative training approaches for structure prediction problems in natural language processing(NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables simple and efficient solutions. However, neglecting the global ordering of the hypothesis list may hinder learning. We propose a listwise learning framework for structure prediction problems such as machine translation. Our framework directly models the entire translation list's ordering to learn parameters which may better fit the given listwise samples. Furthermore, we propose top-rank enhanced loss functions, which are more sensitive to ranking errors at higher positions. Experiments on a large-scale Chinese-English translation task show that both our listwise learning framework and top-rank enhanced listwise losses lead to significant improvements in translation quality.