C o T o H i L i at SIGTYP 2023: Ensemble Models for Cognate and Derivative Words Detection (2023)

Abstract: The identification of cognates and derivatives is a fundamental process in historical linguistics, on which any further research is based. In this paper we present our contribution to the SIGTYP 2023 Shared Task on cognate and derivative detection. We propose a multi-lingual solution based on features extracted from the alignment of the orthographic and phonetic representations of the words.

## introduction and related work in this paper we describe our participation in the sigtyp 2023 shared task on cognate and derivative detection. as both the cornerstone of historical linguistics and a starting point of historical enquiry, automatic detection of cognates and derivatives provides access to a wide range of areas in social sciences @xcite @xcite . concrete examples of the usefulness of accurate prediction of cognates and cognate chains were previously mentioned in the works of @xcite , @xcite , and @xcite through linguistic phylogeny, which in turn can be applied to back tracing linguistic relatedness @xcite . linguistic contact can also be inferred from such predictions @xcite , and this in turn can provide a better understanding and insight into the interaction of ancient communities @xcite . while looking for similar patterns that regulate the cognitive mechanisms involved in semantic change, an extended view on cognate chains can be used as a basis for the identification of meaning divergence @xcite . the study of language acquisition @xcite as well as the challenging problem of removing false friends in machine translation (uban and dinu, 2020) would both benefit from an accurate understanding on the cognate pairings between any two related languages. today there is a vast volume of linguistic data that is yet to be analysed from a historical perspective @xcite . this illustrates the paramount importance of looking into automatic methods and algorithms that can accurately detect cognates and derivatives for both highly resourced and lowly resourced languages. recent years have seen a proliferation of techniques for automated detection of cognate pairs @xcite @xcite @xcite . a lot of these techniques employ feature extraction from various orthographic and phonetic alignments used for training shallow machine learning algorithms in the supervised setting, or used along with clustering methods for the unsupervised approaches @xcite @xcite @xcite @xcite @xcite @xcite . @xcite reported results on cognate detection for several romance language pairs, in which cognate and non-cognate pairs are distinguished via features extracted from orthographic alignments that are used for training support vector machines, with accuracies reaching as high as 87%. deep learning models for cognate detection and other similar tasks were mentioned in fewer studies. siamese convolutional neural networks trained on character sequences for either the orthographic, or the phonetic representations of the words, and augmented with handcrafted features were shown to perform well when tested on cognate prediction for three language families, out of which the most proeminent one being the austronesian family @xcite . also, for borrowing detection @xcite employed deep learning architectures based on recurrent neural networks. ## conclusions in this paper we described our approaches for both the supervised and the unsupervised subtasks from the sigtyp 2023 shared task on cognate and derivative detection. our methods mostly rely on feature engineering powered by sequence alignments for both orthographic and phonetic transcriptions. as we have seen from the results reported on the train labels, the combination of graphic and phonetic features seem to provide better performance than the models relying on one but not the other. one disadvantage is the lack of phonetic transcriptions for some of the low resource languages, which should be an important item in the long list of studies still needed for these type of languages. our submissions for the shared task yielded a macro f1 score of 0.83 for the supervised subtask, which was only 0.04 below the best reported result, and a 0.49 clustering accuracy for the unsupervised subtask, which was the best reported result and achieved a 30% improvement over the baseline. for future work we are considering a qualitative analysis of the errors, in order to better understand on which language pairs our models were registering better results and where they struggled to provide accurate predictions.