Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation
This addresses the problem of inconsistent experimental settings in machine translation research, enabling direct comparisons of new approaches, though it is incremental as it builds on existing benchmarks.
The paper tackles the lack of a standardized benchmark for Chinese-to-English machine translation by proposing a new evaluation setup, and it demonstrates this with a system that outperforms reported results on NIST OpenMT test sets from the past 11 years.
There are many machine translation (MT) papers that propose novel approaches and show improvements over their self-defined baselines. The experimental setting in each paper often differs from one another. As such, it is hard to determine if a proposed approach is really useful and advances the state of the art. Chinese-to-English translation is a common translation direction in MT papers, although there is not one widely accepted experimental setting in Chinese-to-English MT. Our goal in this paper is to propose a benchmark in evaluation setup for Chinese-to-English machine translation, such that the effectiveness of a new proposed MT approach can be directly compared to previous approaches. Towards this end, we also built a highly competitive state-of-the-art MT system trained on a large-scale training set. Our system outperforms reported results on NIST OpenMT test sets in almost all papers published in major conferences and journals in computational linguistics and artificial intelligence in the past 11 years. We argue that a standardized benchmark on data and performance is important for meaningful comparison.