Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation

TL;DR

This paper introduces a cost-focused active learning algorithm to enhance statistical machine translation by efficiently adding data, achieving significantly higher performance improvements even with substantial existing resources.

Contribution

The paper presents a novel active learning approach tailored for large-scale machine translation that overcomes diminishing returns and demonstrates substantial performance gains.

Findings

Order of magnitude increase in improvement rate

Effective data solicitation via Amazon Mechanical Turk

Overcomes diminishing returns in resource-rich scenarios

Abstract

We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement.