Approaching Reflex Predictions as a Classification Problem Using Extended Phonological Alignments (2022)

Abstract: This work describes an implementation of the “extended alignment” model for cognate reflex prediction submitted to the “SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes”. Similarly to List et al. (2022a), the technique involves an automatic extension of sequence alignments with multilayered vectors that encode informational tiers on both site-specific traits, such as sound classes and distinctive features, as well as contextual and suprasegmental ones, conveyed by cross-site referrals and replication. The method allows to generalize the problem of cognate reflex prediction as a classification problem, with models trained using a parallel corpus of cognate sets. A model using random forests is trained and evaluated on the shared task for reflex prediction, and the experimental results are presented and discussed along with some differences to other implementations.

## introduction the special interest group of linguistic typology (sigtyp) organized in 2022 the "sigtyp 2022 shared task on the prediction of cognate reflexes" @xcite , providing the community with cognate-coded wordlists from which varying amounts of cognate sets were withheld. participants were asked to submit models capable of predicting the missing words and morphemes from the non-withheld members. this work describes the submission named ceot-extalign-rf. reflex prediction is particularly interesting to computational historical linguistics, since most approaches to reconstruction are rooted in comparative methods operating on cognate sets @xcite @xcite , i.e., sets of words that are assumed to derive from a common proto-word. we can therefore define a cognate as a member of a set of words (a "cognate set") that share an etymological origin via vertical descent. such a definition highlights how the defining property of a cognate set is the regular sound correspondences between the words involved, even when they are not judged as "similarly sounding" by human evaluators. homologies don't imply homogenies, as processes like word borrowing and chance resemblance can lead to similarities, while true cognates can be very dissimilar, as in the often cited example of english "two" and armenian "erku", both from a reconstructed form *dwóh 1 @xcite . the task can be seen as a form of zero-shot learning @xcite , where a model must learn to predict the "reflexes" of a potentially unknown ancestral word form, with no examples of the relevant cognate set provided during the training phase. when considering the landscape of machine learning methods available and the approaches so far proposed @xcite @xcite @xcite @xcite , including other submissions to this challenge @xcite @xcite , it is possible to identify two main strategies for the task. the first one treats the problem as one of classification, potentially refining sequence results with probabilities from a character model, while the second employs sequence transformation methods, especially those akin to seq2seq approaches @xcite , making the task one analogous to that of "translation". this submission is of the first kind, with results provided by random forests @xcite , but attention should focus on the proposed method of data transformation. the method is based on "multitiered" proposals, originally based on an idea by j.-m. list, which have been described and implemented in other works @xcite @xcite , including for the baseline for the shared task at hand @xcite . it is an approach for modeling historical linguistic relationships developed to solve problems that are not addressed by pure-correspondence approaches, such as those involving cross-site generalization and the need to capture contextual and suprasegmental information. by considering how discrete representations of phonological sequences can be an unsuitable provision, as the latter are abstract representations of both discrete and continuous multidimensional phonological domains, the technique extends base alignments with multilayered vectors, encoding additional and derived features. here, such an informational extension is knowingly and intentionally similar to the analytical frameworks of firthian @xcite and autosegmental phonology @xcite , where sequence representation is given by more than a single string of segments. this work first summarizes the extended alignment technique, with a focus on how it can be used to predict cognate reflexes. then, it describes the experimental setting used for the submission, and how the extended alignment method can apply to the task at hand. it concludes with a discussion on the results and future work. ## implementation and results for the shared task, data and classifiers were prepared according to the workflows described in subsection 2.2.2. due to design decisions aiming at testing the method more than achieving the best performing model, only few additional tiers were used for extending the base alignments. these were the segments and sca sound classes pertinent to each language, with a left and right order of 1 and 2 (i.e., l1, l2, r1, r2), thus increasing four times the number of phonological tiers. indexing tiers related to the position in the alignment, counting both left-to-right and right-to-left, were also added. no pruning or preemptive dimensionality reduction was performed. random forests @xcite @xcite were trained using the default implementation in scikit-learn version 1.1.0 @xcite . other classification methods were explored using samples of the datasets, yielding good performance improvements in the cases of xgboost @xcite , lightgbm @xcite , and multi-layer perceptrons @xcite , particularly when performing hyperparameter optimization @xcite . the decision to only submit the results using the random forest method was based on the time and computational constraints to perform a full training, as well as in the goal of establishing a baseline for this implementation of the method. comparing with both the baseline and other submitted methods, the final results suggest much room for improvement. performance was in general between the baseline and baseline-svm method, mostly due to gains in prediction offered by support vector machines @xcite , for the partitions with most information, degrading rapidly when the amount of withheld data was increased, as illustrated by the global results reported in @xcite . the implementation performed particularly poorly with the datasets bantudvd (greenhill and gray, 2015) (as illustrated by the 0.7053 b-cubes for the 10% partition, versus 0.7835 of the baseline submission), felekesemitic (feleke, 2021) (0.6661 b-cubes for the 10% partition, versus 0.6925 of the baseline). it performed better with datasets beidazihui (běijīng dàxué, 1962) (as illustrated by the 0.8356 b-cubes for the 10% partition, versus 0.7279 of the baseline), bodtkhobwa (bodt and list, 2022) (0.7993 b-cubes for the 10% partition, versus 0.7566 of the baseline), bremerberta (bremer, 2016) (0.7915 b-cubes for the 10% partition, versus 0.7187 of the baseline), deepadungpalaung @xcite (0.8143 b-cubes for the 10% partition, versus 0.7597 of the baseline), wangbai @xcite (0.8326 b-cubes for the 10% partition, versus 0.8048 of the baseline), hattorijaponic @xcite (0.8127 b-cubes for the 10% partition, versus 0.7889 of the baseline), listsamplesize (list, 2014) (0.5325 b-cubes for the 10% partition, versus 0.4048 of the baseline). full results are available along with the submission, with performance for other datasets comparable to the baseline. ## discussion similarly to the implementation in list et al. 2022a, the method of extending alignments implemented here can be conceived as a learned data augmentation strategy, since it is not based on statistical properties of each dataset (that is, each collection of alignment sets), but on the contribution from specialized linguistic knowledge. such a strategy does not exclude the use of purely statistical methods, allowing them to more easily find the correspondences between sets, especially when they convey phonological traits that are sparse and non sequential. once mature, we believe that the strategy will be beneficial for most machine learning methods, as it should allow for increased generalization due to increased complexity of the data sets, incorpo-rating informational tiers beyond phonological or sequence properties. extended alignments have proven their usefulness for tasks like supervised phonological reconstruction and cognate reflex prediction. there are, however, many other problems to which they could also be applied, either by replacing or by complementing existing methods. the probability of correspondence between certain phonemes, for example, can be used for fine-grained decisions in cases of doubts in the domain of sequence alignment, using information which is "local" to the languages under study along with default correspondence score matrices that are generally computed for global usage. likewise, the method can credibly be used in the detection of loanwords @xcite , especially in cases of large exchange of words between two languages and of "learned loanwords". finally, like the other models proposed for this challenge, the method can be used to manage linguistic data, identifying forms that are not predicted by a model trained on the data itself, which may be due to either particularly rich individual histories or different amounts data noise.