Representing and Computing Uncertainty in Phonological Reconstruction (2023)

Abstract: Despite the inherently fuzzy nature of reconstructions in historical linguistics, most scholars do not represent their uncertainty when proposing proto-forms. With the increasing success of recently proposed approaches to automating certain aspects of the traditional comparative method, the formal representation of proto-forms has also improved. This formalization makes it possible to address both the representation and the computation of uncertainty. Building on recent advances in supervised phonological reconstruction, during which an algorithm learns how to reconstruct words in a given proto-language relying on previously annotated data, and inspired by improved methods for automated word prediction from cognate sets, we present a new framework that allows for the representation of uncertainty in linguistic reconstruction and also includes a workflow for the computation of fuzzy reconstructions from linguistic data.

## introduction phonological reconstruction refers to the techniques that linguists use to reconstruct the phonological and phonetic shape of word forms or morphemes in unattested ancestral languages. although the results are inherently provisional (as witnessed by the changes in the fable by schleicher 1868 over the last decades, cf. @xcite , linguists typically present their results in the form of discrete phonological units, giving the impression of exactitude and rigor. thus, although phonological reconstructions change with time as the knowledge or assumptions about a language family change, scholars typically provide the results as if they were final. by focusing on the uncertainty of phonological reconstructions, we aim to provide a new framework by which uncertainty in phonological reconstruction can be a) represented (in etymological databases or etymological dictionaries), and b) computed (from etymological datasets). representation and computation have several benefits. on the one hand, improved representations allow for a more refined reconstruction practice that more directly and consistently indicates the weak points in a reconstruction. on the other hand, computing the uncertainty of a given reconstruction system allows scholars to refine their reconstructions by helping them to identify weak points and potential errors in their cognate judgments or correspondence patterns. the traditional techniques for phonological reconstruction, by which ancestral word forms are reconstructed from observed words with the help of the comparative method, are of crucial importance for historical language comparison. despite the inherently fuzzy nature of reconstructions, most scholars have so far hesitated to systematically represent their uncertainty when proposing protoforms (for an exception see @xcite , and discussions of uncertainty are very spurious in the literature. with the increasing success of recently proposed techniques by which certain aspects of the traditional comparative method can be automated, the formal representation of words, morphemes, cognate sets, and proto-forms has also improved. this makes it possible to address the problems of both the representation and the computation of uncertainty. supervised approaches have led to major advances in automated phonological reconstruction; scholars provide a partially annotated dataset in which a certain number of protoforms are already provided, and an algorithm is then trained on the data in order to propose new proto-forms for so far unobserved cognate sets. this task is very similar to the task of cognate reflex prediction, in which the word forms which have to be predicted are not proto-forms, but word forms from descendant languages, and algorithms have to predict the reflex of a cognate set in a given language based on the sound correspondence patterns and the reflexes of the cognate set in related languages. in the past decade, scholars have proposed quite a few new methods for both cognate reflex prediction and supervised phonological reconstruction. @xcite tested recurrent neural networks on a dataset of romance languages originally compiled by ciobanu and @xcite , reporting very promising results on supervised approaches. this study was later extended by @xcite , who used a transformer architecture @xcite and additionally tested the approach on a dataset of chinese dialect varieties. @xcite proposed a new framework based on support vector machines to predict proto-forms from phonetic alignments, which they tested on six different datasets covering several different language families. in a recently organized shared task on cognate reflex prediction @xcite , @xcite proposed two methods that outperformed alternative approaches, one originally designed for the handling of place name pronunciations in japan @xcite , and one designed for the restoration of digital images in which pixels are missing @xcite . all in all, the most successful methods in the shared task all showed good performance: when retaining 90% of the data for training, the methods differed on average by one sound from the attested word forms. while the task of unsupervised phonological reconstruction, where algorithms would reconstruct a proto-language from cognate sets from scratch, has not been sufficiently investigated so far (an early approach by @xcite was only tested on austronesian languages with the code never published), we can see that phonological reconstruction in a supervised setting has become a real option and could be integrated into computerassisted workflows, in which scholars first annotate parts of their data, then compute new reconstructions automatically, and later refine them again. with respect to the representation of uncertainty in reconstruction, linguists typically adopt ad-hoc solutions for individual language families or individual enterprises. in indo-european studies, scholars express their uncertainty with respect to the three laryngeals (*h 1 , *h 2 or h 3 ) by writing a capital *h. in their reconstruction of old chinese, baxter and sagart (2014) employ a complex notation system that puts uncertain parts of their reconstruction into brackets (with -[n] meaning, for example, that the reconstruction could be either the final -n or to -r). in other cases, scholars mention alternative reconstructions only in comments. while both manual and automated methods are inherently fuzzy with respect to phonological reconstruction, so far, few methods have explicitly embraced fuzziness, trying to present uncertainty in reconstructions explicitly. an exception was the method of @xcite , which offered degrees of uncertainty in the imputation of missing sounds in aligned cognate sets, but the fuzzy reconstructions were not further evaluated or inspected. ## materials we work with three etymological datasets which are coded in cross-linguistic data formats ( https://cldf.clld.org , @xcite , following the lexibank workflow for the handling of multilingual wordlists @xcite . the burmish dataset consists of 8 burmish languages, and 269 etymologies that are reflected in at least two descendant languages with a total of 1,442 reflexes. the data was originally compiled by @xcite and later converted to cldf formats by @xcite and further refined for the present study. it is accessible online at https://github.com/lexibank/hillburmish . the karen dataset consists of 10 karenic languages, and offers 365 etymologies originally proposed by @xcite , which are reflected in at least 2 languages with a total of 2,866 reflexes. the data was also compiled for the study by @xcite and slightly refined for this study. it is accessible online at https:// github.com/lexibank/luangthongkumkaren . the panoan dataset consists of 20 panoan languages, and includes the reconstruction of 514 cognate sets across 470 concepts proposed by @xcite . in total, the dataset features 7,305 reflexes. during the digitization of this dataset, all cognate sets were manually aligned @xcite . it is accessible online at https://github.com/pano-tacanan-history/ oliveiraprotopanoan . ## evaluation since we do not have a clear account on what constitutes a good "fuzzy reconstruction" and what constitutes a bad one, we closely analyzed the fuzzy reconstructions proposed for the three datasets and further investigated the results both quantitatively and qualitatively. in the following, we will thus report on the proportion of fuzzy reconstructions per datasets, the most frequently confused sounds in fuzzy reconstructions, and then report on major problems in the original data revealed through a close inspection of the fuzzy reconstructions proposed for the burmish dataset. ## conclusion in this study, we have introduced some novel ideas regarding the handling of uncertainty in phonological reconstruction in historical linguistics. we have tried to show that it may be useful to transparently record uncertainty not only in classical reconstructions but also in reconstructions proposed by automated approaches. these considerations resulted in the proposal of a new framework for fuzzy reconstructions that allows one to compute fuzzy reconstructions from annotated comparative wordlists. applying this framework to three datasets, two from the sino-tibetan language family (burmish and karenic), as well as on the panoan language family, we have shown how the framework can be used to compute the degree of uncertainty in a given dataset, how frequently confused sounds can be computed, and how an individual inspection of the data reveals major classes of errors in the original data. in the future, we hope to refine our current approach in three ways. first, we want to enhance the individual automatic reconstructions for the burmish data and the karenic data by taking the context of important sounds into account. second, we want to enhance our data by correcting cases where we identified problematic cognate judgments. third, we want to apply our method to more data from other language families in order to see how the approach performs there.