Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation

TL;DR

This paper introduces a listwise learning framework with top-rank enhanced loss functions for statistical machine translation, improving translation quality by directly modeling the entire hypothesis list's order.

Contribution

It proposes a novel listwise optimization approach with top-rank sensitivity, addressing limitations of pairwise methods in machine translation.

Findings

Significant improvements in Chinese-English translation quality.

Effective modeling of entire hypothesis list ordering.

Enhanced sensitivity to top-rank errors in translation.

Abstract

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.