Existing work implementing comparative reconstruction of ancestral languages
(proto-languages) has usually required full supervision. However, historical
reconstruction models are only of practical value if they can be trained with a
limited amount of labeled data. We propose a semisupervised historical
reconstruction task in which the model is trained on only a small amount of
labeled data (cognate sets with proto-forms) and a large amount of unlabeled
data (cognate sets without proto-forms). We propose a neural architecture for
comparative reconstruction (DPD-BiReconstructor) incorporating an essential
insight from linguists' comparative method: that reconstructed words should not
only be reconstructable from their daughter words, but also deterministically
transformable back into their daughter words. We show that this architecture is
able to leverage unlabeled cognate sets to outperform strong semisupervised
baselines on this novel task.

提出了一种半监督历史重建任务，其中模型只在少量有标记数据（原型形式的同源词集）和大量无标记数据（无原型形式的同源词集）上进行训练，并且通过提出的神经架构（DPD-BiReconstructor）能够利用无标记同源词集，在这一新任务上超越强大的半监督基准模型。

半监督神经原语重构

Semisupervised Neural Proto-Language Reconstruction

Protoform reconstruction is the task of inferring what morphemes or words
appeared like in the ancestral languages of a set of daughter languages. Meloni
et al. (2021) achieved the state-of-the-art on Latin protoform reconstruction
with an RNN-based encoder-decoder with attention model. We update their model
with the state-of-the-art seq2seq model: the Transformer. Our model outperforms
their model on a suite of different metrics on two different datasets: their
Romance data of 8,000 cognates spanning 5 languages and a Chinese dataset (Hou
2004) of 800+ cognates spanning 39 varieties. We also probe our model for
potential phylogenetic signal contained in the model. Our code is publicly
available at this https URL

使用 RNN-based encoder-decoder with attention model 和 Transformer 模型进行原型重建，分析了语言谱系启示信号。