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。

变形原型重构

Transformed Protoform Reconstruction

This paper tackles the reduction of redundant repeating generation that is
often observed in RNN-based encoder-decoder models. Our basic idea is to
jointly estimate the upper-bound frequency of each target vocabulary in the
encoder and control the output words based on the estimation in the decoder.
Our method shows significant improvement over a strong RNN-based
encoder-decoder baseline and achieved its best results on an abstractive
summarization benchmark.

本文提出了一种利用编码器中目标词汇的上限频率进行控制从而减少重复生成的 RNN 编码器 - 解码器模型，并在提取摘要任务上取得了显著的改进。