Papers › Transformed Protoform Reconstruction

Transformed Protoform Reconstruction

4 Jul 2023arXiv:2307.01896archive 2025-07-28

Young Min Kim, Kalvin Chang, Chenxuan Cui, David Mortensen

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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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout LSTM Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Seq2Seq Sigmoid Activation Softmax Tanh Activation Transformer

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