B ayesian Phylogenetic Cognate Prediction (2022)

Abstract: In Jäger (2019) a computational framework was defined to start from parallel word lists of related languages and infer the corresponding vocabulary of the shared proto-language. The SIGTYP 2022 Shared Task is closely related. The main difference is that what is to be reconstructed is not the proto-form but an unknown word from an extant language. The system described here is a re-implementation of the tools used in the mentioned paper, adapted to the current task.

## introduction in @xcite i presented a pilot study of a computational historical linguistics workflow. starting from parallel word lists (taken from @xcite ) of 29 romance languages and dialects, covering 40 core concepts, it produced reconstructions of the proto-romance words for the same concepts. the intermediate steps of this workflow are 1. for each concept, cluster the corresponding sound strings into cognate classes, 2. infer a posterior distribution of phylogenies of the covered doculects using bayesian inference, 3. apply bayesian inference to identify the maximum a posteriori cognate class at the root of the tree for each concept (ancestral state reconstruction, asr), 4. apply multiple sequence alignment (msa) to the words of each cognate class, 5. apply asr to each alignment column of the msas of the cognate classes identified in step 3; gaps are treated as regular characters, and 6. concatenate the reconstructions and removing gaps. the result turned out to be an imperfect but reasonable approximations of the attested latin wordlist. the sigtyp 2022 shared task on the prediction of cognate reflexes ( https://github.com/ sigtyp/st2022 , @xcite ) is very similar in nature. the system described here is an adaptation of @xcite workflow to this task. ## data the authors of the shared task made parallel word lists for 20 language families available. for details of the provinence of the data and the pre-processing steps performed, see @xcite . each dataset comprises between four and 19 related languages, and between 500 and ca. 10,000 words. words are classified according to cognate classes, which are based either on expert judgments or are inferred via automatic cognate detection. no information about the meanings of the words are available for training or inference. all words are transcribed in ipa and tokenized. the data are arranged in a table with cognate classes as rows and languages as columns. in each dataset was split into a training set and a test set. the proportion of test data was varied between 10%, 20%, 30%, 40% and 50%, leading to a total of 50 datasets, each consisting of a training and a test set. for the test data, one word per row was masked, using each attested word for masking in turn. the task is to predict the masked words from the other cognates in the same row. ## methods this task differs from the one described in @xcite mainly by the fact that not some ancestral word form has to be inferred but a word from an extant language. for the particular inference methods used, this difference is actually inessential, since it is based on a time-reversible model of language change. the first step of the workflow by @xcite , identifying cognate classes, has already been performed here. this led to the following workflow: each of these steps will be briefly explained in the following subsections. ## discussion let me close with a brief reflection on what kind of information this system extracts from the training set to perform cognate prediction. there are mainly two patterns the system pays attention to. the first is the regularity of sound correspondences which are encapsulated in the emission probabilites of the trained phmm, especially its m state. the system does not pay attention to the specific languages the words to be aligned come from, so it is unaware of language-specific sound correspondences. therefore the prediction step does not make use of specific sound laws in any way. second, the system employs phylogenetic information. this amounts to a weighing of the importance of the cognates from other languages when deciding on the choice of the missing value imputation. also, since the missing value imputation is performed column-wise for the alignment matrix, no syntagmatic information is being used. it is not checked which candidate predictions are phonotactically or morphologically most similar to the training words from the same languages. in future research, it is worth considering to extend the system towards the usage of languagespecific sound correspondences and syntagmatic information.