变形原形重建

Young Min Kim, Kalvin Chang, Chenxuan Cui, David R. Mortensen
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引用次数: 1

摘要

原形重建的任务是推断语素或单词在一组子语言的祖先语言中出现的样子。Meloni等人(2021)使用基于rnn的编码器-解码器和注意力模型实现了最先进的拉丁语原始形式重建。我们用最先进的seq2seq模型来更新他们的模型:变形金刚。我们的模型在两个不同数据集上的一套不同指标上优于他们的模型:他们的罗曼语数据包含5种语言的8000个同源词,而中文数据集(Hou 2004)包含800多个同源词,涵盖39个品种。我们还探索了模型中包含的潜在系统发育信号。我们的代码可以在https://github.com/cmu-llab/acl-2023上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transformed Protoform 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 https://github.com/cmu-llab/acl-2023.
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