论文标题
光束搜索的简单基线
A Simple Baseline for Beam Search Reranking
论文作者
论文摘要
机器翻译中的重读方法旨在缩小常见评估指标（例如BLEU）与最大似然学习和解码算法之间的差距。先前的工作通过培训模型根据其预测的BLEU分数来应对重读梁搜索候选者的挑战，这是基于在大型单语言中鉴定的大型模型的基础，这是基线翻译模型从未获得的特权。在这项工作中，我们研究了一种简单的方法，用于培训Rerankers在不引入其他数据或参数的情况下预测候选候选者的BLEU分数。我们的方法可以用作干净的基线，与外部因素解耦，以供将来在该领域进行研究。
Reranking methods in machine translation aim to close the gap between common evaluation metrics (e.g. BLEU) and maximum likelihood learning and decoding algorithms. Prior works address this challenge by training models to rerank beam search candidates according to their predicted BLEU scores, building upon large models pretrained on massive monolingual corpora -- a privilege that was never made available to the baseline translation model. In this work, we examine a simple approach for training rerankers to predict translation candidates' BLEU scores without introducing additional data or parameters. Our approach can be used as a clean baseline, decoupled from external factors, for future research in this area.
