Protolanguage reconstruction is central to historical linguistics. The
comparative method, one of the most influential theoretical and methodological
frameworks in the history of the language sciences, allows linguists to infer
protoforms (reconstructed ancestral words) from their reflexes (related modern
words) based on the assumption of regular sound change. Not surprisingly,
numerous computational linguists have attempted to operationalize comparative
reconstruction through various computational models, the most successful of
which have been supervised encoder-decoder models, which treat the problem of
predicting protoforms given sets of reflexes as a sequence-to-sequence problem.
We argue that this framework ignores one of the most important aspects of the
comparative method: not only should protoforms be inferable from cognate sets
(sets of related reflexes) but the reflexes should also be inferable from the
protoforms. Leveraging another line of research -- reflex prediction -- we
propose a system in which candidate protoforms from a reconstruction model are
reranked by a reflex prediction model. We show that this more complete
implementation of the comparative method allows us to surpass state-of-the-art
protoform reconstruction methods on three of four Chinese and Romance datasets.

通过运用反射预测模型对重构模型中的候选原型进行重新排序，我们的研究在三个中国和罗曼语数据集中超越了最先进的原型重建方法。