Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study (2023)

Abstract: We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from phones to their articulatory features and (3) a typological database of sound changes.We train a neural network on these sound change data to weight articulatory distances between phones and predict intermediate sound change steps between historical protoforms and their modern descendants, replacing a linguistic expert in part of a parsimony-based phylogenetic inference algorithm. In our best experiments on Tukanoan languages, this method produces trees with a Generalized Quartet Distance of 0.12 from a tree that used expert annotations, a significant improvement over other semi-automated baselines. We discuss potential benefits and drawbacks to our neural approach and parsimony-based tree prediction. We also experiment with a minimal generalization learner for automatic sound law induction, finding it less effective than sound laws from expert annotation. Our code is publicly available.

## introduction languages and biological species evolve in interestingly analogous ways. both display variation in space and time that may be inherited or innovated. as in biology, each node in a linguistic phylogenetic (family) tree corresponds to one or more innovations ("mutations"). typically, linguists infer these phylogenies by finding patterns of innovations in pronunciation, or sound changes. sound laws, or rules that define sound changes and the contexts in which they occur, apply to all instances of a sound in a given context. (for example, all instances of proto-west germanic [*t] be-came [t ͡ s] (written as z) at the beginning of words in high german, while english was unaffected. this resulted in the english-german cognate pairs zehn : ten, zoll : toll, and zahn : tooth.) because these laws have few exceptions, they can work as a basis for modeling historical language change. linguists typically infer phylogenies by constructing the tree that maps from the ancestor language at the root to the daughter languages via the most probable system of these sound laws @xcite . existing partially automated approaches to this method require multiple sets of expert annotations. we attempt to alleviate this via proposed methods that incorporate even more automation. below we discuss the necessity of both sound laws and sound changes in predicting phylogenies, which we automatically infer in our proposed methods. linguists induce sound laws by aligning cog-nates (words with a common ancestor) by phoneme. from this alignment they extract sound correspondences, or sets of sounds in the same context that likely evolved from the same sound in the proto-language. (for example, at the beginning of words, there is a correspondence between high german [t ͡ s], dutch @xcite , english @xcite , swedish @xcite , and icelandic [t]). they then reconstruct protophonemes for each set of aligned cognates (in our germanic example, this happens to be [*t]). the posited sound laws from this process enable deterministic derivation of the daughter forms from the reconstructed protoforms, or words in the protolanguage. inducing sound laws is central to sound change-based phylogenetics. we experiment with both algorithms that predict these sound laws automatically and those that need them to be provided by a linguist. beyond sound laws alone, however, phylogenetic inference algorithms must consider how sounds evolve and branch off through inter-mediate sound changes over time. sound change emerges from phonetic variation, as speakers modify their pronunciation along acoustic or articulatory dimensions @xcite . because some variations in pronunciation occur more frequently than others, not all sound changes are equally probable. in particular, the probability of a sound change (e.g. @xcite ) is often different from that of its reverse (e.g. [f] becoming @xcite ), a property known as the directionality of sound change @xcite . because phonetic variation is gradual (with few articula-tory features-or fundamental characteristics of pronunciation-changing at a time) @xcite , sound change often results in phonetically similar sounds across cognates and their corresponding protoform. larger apparent jumps in pronunciation from proto-phoneme (ancestral sound) to reflex (descendant sound) are often the result of smaller changes over time, or intermediate sound changes @xcite . for example, @xmath0. intermediate paths from a proto-sound to different daughter reflexes can overlap, which enables identifying innovations that are shared among daughters (shared innovations). ## related work unlike our work, prior phylolinguistic work mostly inferred a tree from a boolean cognacy matrix that shows which synonymous words come from the same ancestral word @xcite . however, cognacy is complicated by language contact that leads to the borrowing of words, as opposed to their inheritance @xcite . @xcite criticized such use of cognacy information in phylogenetic inference, and called on computational methods to use shared innovations as linguists do. @xcite heeded this call and manually derived shared innovations for proto-min and its modern daughters, finally running a maximum parsimony algorithm from felsenstein (2013) on these shared innovations. however, their shared innova-tion matrix is binary and does not encode the direction and frequency of sound changes as our methods do. hruschka et al. (2015) jointly inferred phylogeny and reconstructed protoforms with markov chain monte carlo (mcmc) using phonological data from turkic languages, where the tree likelihood was conditioned on reconstructed protoforms. however, their sound laws are all contextfree. @xcite perform joint phylogenetic, protoform reconstruction, and cognate inference for 14 polynesian languages using mcmc, but the expressive power of their model is limited to only cvcv sequences for alignment without insertions or deletions or sound law contexts. unlike these approaches, our methods (both with and without asli) include in-context sound laws and can process all sound sequences. we are also not the first researchers to explore neural modeling of phonetic features. @xcite showed that neural networks can predict features of proto-indo-european phones given the features of a trigram context, which reflect syn-chronic (applying at a particular stage in a language's history) phonetic phenomena. our neural network, on the other hand, predicts the probability of feature changes in a sound change, given di-achronic data (data that represents change over time). in recent years, there has also been existing work on asli. luo (2021) used reinforcement learning with hierarchical monte carlo tree search to induce sound laws for germanic, romance, and slavic. to our knowledge, they are the first to propose an asli method. @xcite also attempted automatic induction of sound correspondences, though these correspondences lacked the contexts associated with actual sound laws. the minimum generalization learner we employ for asli @xcite , in contrast, was originally designed to induce synchronic morphological rules and is deterministic. ## methodology we incorporate aiscp and asli into @xcite 's diwest method for phylogenetic inference, which involves directed, weighted phone transitions. the authors describe it as a directed version of sankoff parsimony @xcite . their maximum parsimony algorithm searches for trees by trying different sound transitions (interme-diate sound changes) along the branches of trees that minimize the total transition cost. by making the cost of sound changes asymmetrical in a sound change transition matrix, their method captures the directionality of sound change, yielding a rooted tree. to search the large space of possible trees, they used a genetic search algorithm that balances exploration (iterating through a subset of possible trees) and exploitation (incrementally mutating the current best trees). chacon and list (2016 genetic inference using the transition matrix from the step above 5. obtain a consensus tree our algorithm follows this same framework. our contribution is to automate the expert's annotations in step 3, and in steps 1-2 in some experiments. section 3.1 outlines the way both chacon and list (2016)'s algorithm and our modifications of it incorporate intermediate sound changes via a sound transition matrix. section 3.2 elaborates on replacing the expert in step 3 with aiscp. section 3.3 outlines replacing the expert in steps 1-2 with asli. ## results and discussion our findings suggest that parsimony does not correlate with gqd, with spearman's 𝜌 = -0.04 across our experiments (figure 6 ). the parsimony of our best tree overall was not even better than the median across the 10 runs of its experiment. it seems relying only on parsimony to predict phylogenies is not guaranteed-or perhaps even likelyto produce optimal trees. the high variance across experiments is likely due to @xcite 's genetic search algorithm starting with random trees and at times getting stuck in sub-optimal areas of the search space. ## conclusion and future work we propose a novel method to automatically predict intermediate sound changes for phylogenetic inference, via neural weighting of feature-based edit distance between phones. when we apply our method with a single-layer network, we accurately predict 88% of binary-branching language quartets (butterflies) in a gold tukanoan phylogeny. furthermore, our typologically informed neural approach based on articulatory features produces intermediate sound changes that capture expert intuitions on phonetic naturalness. our analysis shows that not only does phonetic plausibility matter, but so does the accuracy of sound transition costs for successful phylogenetic inference. we also present a method to predict sound laws automatically via minimal generalization, which creates less generalizable sound laws than the expert. future work in this vein may involve exploration of other asli approaches @xcite , pruning the phone graph using phoible to focus on cross-linguistically frequent phonemes @xcite , generalization of our approach to other language families such as polynesian, and incorporation of mcmc methods to jointly reconstruct protoforms and phylogenies.