Verba volant, scripta volant? Don't worry! There are computational solutions for protoword reconstruction
Authors: Liviu P Dinu, Ana Sabina Uban, Alina Maria Cristea, Ioan-Bogdan Iordache, Teodor Marchitan, Simona Georgescu, Laurentiu Zoicas


Reviewer paper summaries: This paper presents a method to reconstruct proto-words (cognates) in 5 roman languages, trained with the existing RoBoCop database, with a number of character-level methods (CRF, LSTM, transformers, LLM T5). Two evaluation are conducted, the first one on the writing of proto-words, the second one on a phonemic representation generated by a speech library. The experiments highlights the difficulty of the task, predicting about half of the database protowords, with better results for graphemic over phenmic. Results are discussed considering the fact that some predicted protowords may be near-misses or  maybe accurate cognates that absent from the RoBoCop database. Some linguistic phenomenon are discussed on both graphemic and phonemic results.
Summary of strengths: - Study combining linguistic consideration with machine-learning techniques
- Experiments with various character-based machine-learning methods
- Evaluations on both graphemic and phonemic distances
- Interesting linguistic discussion about protowords and their relevance
Summary of weaknesses: - No human evaluation of (a sample of) generated proto-words
- Speech conversion and reliability is not much discussed
- Metrics do not provided quantified view of linguistic phenomenon
Comments, suggestions and typos: This paper is interesting, especially because it combines linguistic and machine learning considerations. This phonemic evaluations highly rely on a library, which reliability is not assessed and almost not discussed, it would be worth adding corresponding considerations. It would be nice to have a quantified human evaluation of the results over a sample of generated proto-words, to see how much the predicted proto-words are acceptable from a human point of view. Providing metrics per language and per phenomenon would be nice.
Overall assessment scores: 4