Typology of sound change (Open problems in computational diversity linguistics 9)

We are getting closer to the end of my list of open problems in computational diversity linguistics. After this post, there is only one left, for November, followed by an outlook and a wrap-up in December.

In last month's post, devoted to the Typology of semantic change, I discussed the general aspects of a typology in linguistics, or — to be more precise — how I think that linguists use the term. One of the necessary conditions for a typology to be meaningful is that the phenomenon under questions shows enough similarities across the languages of the world, so that patterns or tendencies can be identified regardless of the historical relations between human languages.

Sound change in this context refers to a very peculiar phenomenon observed in the change of spoken languages, by which certain sounds in the inventory of a given language change their pronunciation over time. This often occurs across all of the words in which these sounds recur, or across only those sounds which appear to occur in specific phonetic contexts.

As I have discussed this phenomenon in quite a few past blog posts, I will not discuss it any more here, but I will rather simply refer to the specific task, that this problem entails:

Assuming (if needed) a given time frame, in which the change occurs, establish a general typology that informs about the universal tendencies by which sounds occurring in specific phonetic environments are subject to change.

Note that my view of "phonetic environment" in this context includes an environment that would capture all possible contexts. When confronted with a sound change that seems to affect a sound in all phonetic contexts, in which the sound occurs in the same way, linguists often speak of "unconditioned sound change", as they do not find any apparent condition for this change to happen. For a formal treatment, however, this is unsatisfying, since the lack of a phonetic environment is also a specific condition of sound change.

Why it is hard to establish a typology of sound change

As is also true for semantic change, discussed as Problem 8 last month, there are three major reasons why it is hard to establish a typology of sound change. As a first problem, we find, again, the issue of acquiring the data needed to establish the typology. As a second problem, it is also not clear how to handle the data appropriately in order to allow us to study sound change across different language families and different times. As a third problem, it is also very difficult to interpret sound change data when trying to identify cross-linguistic tendencies.

Problem 1

The problem of acquiring data about sound change processes in sufficient size is very similar to the problem of semantic change: most of what we know about sound change has been inferred by comparing languages, and we do not know how confident we can be with respect to those inferences. While semantic change is considered to be notoriously difficult to handle (Fox 1995: 111), scholars generally have more confidence in sound change and the power of linguistic reconstruction. The question remains, however, as to how confident we can really be, which divides the field into the so-called "realists" and the so-called "abstractionalists" (see Lass 2017 for a recent discussion of the debate).

As a typical representative of abstractionalism in linguistic reconstruction, consider the famous linguist Ferdinand de Saussure, who emphasized that the real sound values which scholars reconstructed for proposed ancient words in unattested languages like, for example, Indo-European, could as well be simply replaced by numbers or other characters, serving as identifiers (Saussure 1916: 303). The fundamental idea here, when reconstructing a word for a given proto-language, is that a reconstruction does not need to inform us about the likely pronunciation of a word, but rather about the structure of the word in contrast to other words.

This aspect of historical linguistics is often difficult to discuss with colleagues from other disciplines, since it seems to be very peculiar, but it is very important in order to understand the basic methodology. The general idea of structure versus substance is that, once we accept that the words in a languages are built by drawing letters from an alphabet, the letters themselves do not have a substantial value, but have only a value in contrast to other letters. This means that a sequence, such as "ABBA" can be seen as being structurally identical with "CDDC", or "OTTO". The similarity should be obvious: we have the same letter in the beginning and the end of each word, and the same letter being repeated in the middle of each word (see List 2014: 58f for a closer discussion of this type of similarity).

Since sequence similarity is usually not discussed in pure structural terms, the abstract view of correspondences, as it is maintained by many historical linguists, is often difficult to discuss across disciplines. The reason why linguists tend to maintain it is that languages tend to change not only their words by mutating individual sounds, but that whole sound systems change, and new sounds can be gained during language evolution, or lost (see my blogpost from March 2018 for a closer elaboration of the problem of sound change).

It is important to emphasize, however, that despite prominent abstractionalists such as Ferdinand de Saussure (1857-1913), and in part also Antoine Meillet (1866-1936), the majority of linguists think more realistically about their reconstructions. The reason is that the composition of words based on sounds in the spoken languages of the world usually follows specific rules, so-called phonotactic rules. These may vary to quite some degree among languages, but are also restricted by some natural laws of pronunciability. Thus, although languages may show impressively long chains of one consonant following another, there is a certain limit to the number of consonants that can follow each other without a vowel. Sound change is thus believed to originate roughly in either production (speakers want to pronounce things in a simpler, more convenient way) or perception (listeners misunderstand words and store erroneous variants, see Ohala 1989 for details). Therefore, a reconstruction of a given sound system based on the comparison of multiple languages gains power from a realistic interpretation of sound values.

The problem with the abstractionalist-realist debate, however, is that linguists usually conduct some kind of a mixture between the two extremes. That means that they may reconstruct very concrete sound values for certain words, where they have very good evidence, but at the same time, they may come up with abstract values that serve as place holders in lack of better evidence. The most famous example are the Indo-European "laryngeals", whose existence is beyond doubt for most historical linguistics, but whose sound values cannot be reconstructed with high reliability. As a result, linguists tend to spell them with subscript numbers as *h₁, *h₂, and *h₃. Any attempt to assemble data about sound change processes in the languages of the world needs to find a way to cope with the different degrees of evidence we find in linguistic analyses.

Problem 2

This leads us directly to our second problem in handling sound change data appropriately in order to study sound change processes. Given that many linguists propose changes in the typical A > B / C (A becomes B in context C) notation, a possible way of thinking about establishing a first database of sound changes would consist of typing these changes from the literature and making a catalog out of it. Apart from the interpretation of the data in abstractionalist-realist terms, however, such a way of collecting the data would have a couple of serious shortcomings.

First, it would mean that the analysis of the linguist who proposed the sound change is taken as final, although we often find many debates about the specific triggers of sound change, and it is not clear whether there would be alternative sound change rules that could apply just as well (see Problem 3 on the task of automatic sound law induction for details). Second, as linguists tend to report only what changes, while disregarding what does not change, we would face the same problem as in the traditional study of semantic change: the database would suffer from a sampling bias, as we could not learn anything about the stability of sounds. Third, since sound change depends not only on production and perception, but also on the system of the language in which sounds are produced, listing sounds deprived of examples in real words would most likely make it impossible to take these systemic aspects of sound change into account.

Problem 3

This last point now leads us to the third general difficulty, the question of how to interpret sound change data, assuming that one has had the chance to acquire enough of it from a reasonably large sample of spoken languages. If we look at the general patterns of sound change observed for the languages of the world, we can distinguish two basic conditions of sound change, phonetic conditions and systemic conditions. Phonetic conditions can be further subdivided into articulatory (= production) and acoustic (= perception) conditions. When trying to explain why certain sound changes can be observed more frequently across different languages of the world, many linguists tend to invoke phonetic factors. If the sound p, for example, turns into an f, this is not necessarily surprising given the strong similarity of the sounds.

But similarity can be measured in two ways: one can compare the similarity with respect to the production of a sound by a speaker, and with respect to the perception of the sound by a listener. While production of sounds is traditionally seen as the more important factor contributing to sound change (Hock 1991: 11), there are clear examples for sound change due to misperception and re-interpretation by the listeners (Ohala 1989: 182). Some authors go as far as to claim that production-driven changes reflect regular internal language change (which happens gradually during acquisition, or (depending on the theory) also in later stages (Bybee 2002), while perception-based changes rather reflect change happening in second language acquisition and language contact (Mowrey and Pagliuca 1995: 48).

While the interaction of production and perception has been discussed in some detail in the linguistic literature, the influence of systemic factors has so far been only rarely regarded. What I mean by this factor is the idea that certain changes in language evolution may be explained exclusively as resulting from systemic constellations. As a straightforward example, consider the difference in design space for the production of consonants, vowels, and tones. In order to maintain pronunciability and comprehensiblity, it is useful for the sound system of a given language to fill in those spots in the design space that are maximally different from each other. The larger the design space and the smaller the inventory, the easier it is to guarantee its functionality. Since design spaces for vowels and tones are much smaller than for consonants, however, these sub-systems are more easily disturbed, which could be used to explain the presence of chain shifts of vowels, or flip- flop in tone systems (Wang 1967: 102). Systemic considerations play an increasingly important role in evolutionary theory, and, as shown in List et al. (2016), also be used as explanations for phenomena as strange as the phenomenon of Sapir's drift (Sapir 1921).

However, the crucial question, when trying to establish a typology of sound change, is how these different effects could be measured. I think it is obvious that collections of individual sound changes proposed in the literature are not enough. But what data would be sufficient or needed to address the problem is not entirely clear to me either.

Traditional approaches

As the first traditional approach to the typology of sound change, one should mention the intuition inside the heads of the numerous historical linguists who study particular language families. Scholars trained in historical linguistics usually start to develop some kind of intuition about likely and unlikely tendencies in sound change, and in most parts they also agree on this. The problem with this intuition, however, is that it is not explicit, and it seems even that it was never the intention of the majority of historical linguists to make their knowledge explicit. The reasons for this reluctance with respect to formalization and transparency are two-fold. First, given that every individual has invested quite some time in order to grow their intuition, it is possible that the idea of having a resource that distributes this intuition in a rigorously data-driven and explicit manner yields the typical feeling of envy in quite a few people who may then think: «I had to invest so much time in order to learn all this by heart. Why should young scholars now get all this knowledge for free?» Second, given the problems outlined in the previous section, many scholars also strongly believe that it is impossible to formalize the problem of sound change tendencies.

The by far largest traditional study of the typology of sound change is Kümmel's (2008) book Konsonantenwandel (Consonant Change), in which the author surveys sound change processes discussed in the literature on Indo-European and Semitic languages. As the title of the book suggests, it concentrates on the change of consonants, which are (probably due to the larger design space) also the class of sounds that shows stronger cross-linguistic tendencies. The book is based on a thorough inspection of the literature on consonant change in Indo-European and Semitic linguistics. The procedure by which this collection was carried out can be seen as the gold standard, which any future attempt of enlarging the given collection should be carried out.

What is specifically important, and also very difficult to achieve, is the harmonization of the evidence, which is nicely reflected in Kümmel's introduction, where he mentions that one of the main problems was to determine what the scholars actually meant with respect to phonetics and phonology, when describing certain sound changes (Kümmel 2008: 35). The major drawback of the collection is that it is not (yet) available in digital form. Given the systematicity with which the data was collected, it should be generally possible to turn the collection into a database; and it is beyond doubt that this collection could offer interesting insights into certain tendencies of sound change.

Another collection of sound changes collected from the literature is the mysterious Index Diachronica, a collection of sound changes collected from various language families by a person who wishes to remain anonymous. Up to now, this collection even has a Searchable Index that allows scholars to click on a given sound and to see in which languages this sound is involved in some kind of sound change. What is a pity about the resource is that it is difficult to use, given that one does not really know where it actually comes from, and how the information was extracted from the sources. If the anonymous author would only decide to put it (albeit anonymously, or under a pseudonym) on a public preprint server, such as, for example, Humanities Commons, this would be excellent, as it would allow those who are interested in pursuing the idea of collecting sound changes from the literature an excellent starting point to check the sources, and to further digitize the resource.

Right now, this resource seems to be mostly used by conlangers, ie., people who create artificial languages as a hobby (or profession). Conlangers are often refreshingly pragmatic, and may come up with very interesting and creative ideas about how to address certain data problems in linguistics, which "normal" linguists would refuse to do. There is a certain tendency in our field to ignore certain questions, either because scholars think it would be too tedious to collect the data to address that problem, or they consider it impossible to be done "correctly" from the start.

As a last and fascinating example, I have to mention the study by Yang and Xu (2019), in which the authors review studies of concrete examples of tone change in South-East Asian languages, trying to identify cross-linguistic tendencies. Before I read this study, I was not aware that tone change had at all been studied concretely, since most linguists consider the evidence for any kind of tendency far too shaky, and reconstruct tone exclusively as an abstract entity. The survey by Yang and Xu, however, shows clearly that there seem to be at least some tendencies, and that they can be identified by invoking a careful degree of abstraction when comparing tone change across different languages.

For the detailed reasons outlined in the previous paragraph, I do not think that a collection of sound change examples from the literature addresses the problem of establishing a typology of sound change. Specifically, the fact that sound change collections usually do not provide any tangible examples or frequencies of a given sound change within the language where it occurred, but also the fact that they do not offer any tendencies of sounds to resist change, is a major drawback, and a major loss of evidence during data collection. However, I consider these efforts as valuable and important contributions to our field. Given that they allow us to learn a lot about some very general and well-confirmed tendencies of sound change, they are also an invaluable source of inspiration when it comes to working on alternative approaches.

Computational approaches

To my knowledge, there are no real computational approaches to the study of sound change so far. What one should mention, however, are initial attempts to measure certain aspects of sound change automatically. Thus, Brown et al. (2013) measure sound correspondences across the world's languages, based on a collection of 40-item wordlists for a very large sample of languages. The limitations of this study can be found in the restricted alphabet being used (all languages are represented by a reduced transcription system of some 40 letters, called the ASJP code. While the code originally allowed representing more that just 40 sounds, since the graphemes can be combined, the collection was carried out inconsistently for different languages, which has now led to the situation that the majority of computational approaches treat each letter as a single sound, or consider only the first element of complex grapheme combinations.

While sound change is a directional process, sound correspondences reflect the correspondence of sounds in different languages as a result of sound change, and it is not trivial to extract directional information from sound correspondence data alone. Thus, while the study of Brown et al. is a very interesting contribution, also providing a very straightforward methodology, it does not address the actual problem of sound change.

The study also has other limitations. First, the approach only measures those cases where sounds differ in two languages, and thus we have the same problem that we cannot tell how likely it is that two identical sounds correspond. Second, the study ignores phonetic environment (or context), which is an important factor in sound change tendencies (some sound changes, for example, tend to occur only in word endings, etc.). Third, the study considers only sound correspondences across language pairs, while it is clear that one can often find stronger evidence for sound correspondences when looking at multiple languages (List 2019).

Initial ideas for improvement

What we need in order to address the problem of establishing a true typology of sound change processes, are, in my opinion:

1. a standardized transcription system for the representation of sounds across linguistic resources,
2. increased amounts of readily coded data that adhere to the standard transcription system and list cognate sets of ancestral and descendant languages,
3. good, dated phylogenies that allow to measure how often sound changes appear in a certain time frame,
4. methods to infer the sound change rules (Problem 3), and
5. improved methods for ancestral state reconstruction that would allow us to identify sound change processes not only for the root and the descendant nodes, but also for intermediate stages.

It is possible that even these five points are not enough yet, as I am still trying to think about how one should best address the problem. But what I can say for sure is that one needs to address the problem step by step, starting with the issue of standardization — and that the only way to account for the problems mentioned above is to collect the pure empirical evidence on sound change, not the summarized results discussed in the literature. Thus, instead of saying that some source quotes that in German, the t became a ts at some point, I want to see a dataset that provides this in the form of concrete examples that are large enough to show the regularity of the findings and ideally also list the exceptions.

The advantage of this procedure is that the collection is independent of the typical errors that usually occur when data are collected from the literature (usually also by employing armies of students who do the "dirty" work for the scientists). It would also be independent of individual scholars' interpretations. Furthermore, it would be exhaustive — that is, one could measure not only the frequency of a given change, but also the regularity, the conditioning context, or the systemic properties

The disadvantage is, of course, the need to acquire standardized data in a large-enough size for a critical number of languages and language families. But, then again, if there were no challenges involved in this endeavor, I would not present it as an open problem of computational diversity linguistics.

Outlook

With the newly published database of Cross-Linguist Transcription Systems (CLTS, Anderson et al. 2018), the first step towards a rigorous standardization of transcription systems has already been made. With our efforts towards a standardization of wordlists that can also be applied in the form of a retro-standardization to existing data (Forkel et al. 2018), we have proposed a further step of how lexical data can be collected efficiently for a large sample of the worlds' spoken languages (see also List et al. 2018). Work on automated cognate detection and workflows for computer-assisted language comparison has also drastically increased the efficiency of historical language comparison.

So, we are advancing towards a larger collection of high-quality and historically compared datasets; and it is quite possible that we will, in a couple of years from now, arrive at a point where the typology of sound change is no longer a dream by me and many colleagues, but something that may actually be feasible to extract from cross-linguistic data that has been historically annotated. But until then, many issues still remain unsolved; and in order to address these, it would be useful to work towards pilot studies, in order to see how well the ideas for improvement, outlined above, can actually be implemented.

References

Anderson, Cormac and Tresoldi, Tiago and Chacon, Thiago Costa and Fehn, Anne-Maria and Walworth, Mary and Forkel, Robert and List, Johann-Mattis (2018) A Cross-Linguistic Database of Phonetic Transcription Systems. Yearbook of the Poznań Linguistic Meeting 4.1: 21-53.

Brown, Cecil H. and Holman, Eric W. and Wichmann, Søren (2013) Sound correspondences in the worldś languages. Language 89.1: 4-29.

Bybee, Joan L. (2002) Word frequency and context of use in the lexical diffusion of phonetically conditioned sound change. Language Variation and Change 14: 261-290.

Forkel, Robert and List, Johann-Mattis and Greenhill, Simon J. and Rzymski, Christoph and Bank, Sebastian and Cysouw, Michael and Hammarström, Harald and Haspelmath, Martin and Kaiping, Gereon A. and Gray, Russell D. (2018) Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics. Scientific Data 5.180205: 1-10.

Fox, Anthony (1995) Linguistic Reconstruction. An Introduction to Theory and Method. Oxford: Oxford University Press.

Hock, Hans Henrich (1991) Principles of Historical Linguistics. Berlin: Mouton de Gruyter.

Kümmel, Martin Joachim (2008): Konsonantenwandel [Consonant change]. Wiesbaden:Reichert.
Lass, Roger (2017): Reality in a soft science: the metaphonology of historical reconstruction. Papers in Historical Phonology 2.1: 152-163.

List, Johann-Mattis (2014) Sequence Comparison in Historical Linguistics. Düsseldorf: Düsseldorf University Press.

List, Johann-Mattis and Pathmanathan, Jananan Sylvestre and Lopez, Philippe and Bapteste, Eric (2016) Unity and disunity in evolutionary sciences: process-based analogies open common research avenues for biology and linguistics. Biology Direct 11.39: 1-17.

List, Johann-Mattis and Greenhill, Simon J. and Anderson, Cormac and Mayer, Thomas and Tresoldi, Tiago and Forkel, Robert (2018) CLICS². An improved database of cross-linguistic colexifications assembling lexical data with help of cross-linguistic data formats. Linguistic Typology 22.2: 277-306.

List, Johann-Mattis (2019): Automatic inference of sound correspondence patterns across multiple languages. Computational Linguistics 1.45: 137-161.

Mowrey, Richard and Pagliuca, William (1995) The reductive character of articulatory evolution. Rivista di Linguistica 7: 37–124.

Ohala, J. J. (1989) Sound change is drawn from a pool of synchronic variation. In: Breivik, L. E. and Jahr, E. H. (eds.) Language Change: Contributions to the Study of its Causes. Berlin: Mouton de Gruyter., pp.173-198.

Sapir, Edward (1921[1953]) Language. An Introduction to the Study of Speech.

de Saussure, Ferdinand (1916) Cours de linguistique générale. Lausanne: Payot.

William S-Y. Wang (1967) Phonological features of tone. International Journal of American Linguistics 33.2: 93-105.

Yang, Cathryn and Xu, Yi (2019) A review of tone change studies in East and Southeast Asia. Diachronica 36.3: 417-459.

Automatic detection of borrowing (Open problems in computational diversity linguistics 2)

The second task on my list of 10 open problems in computational diversity linguistics deals with detecting borrowings or language contact. The prototypical case of language contact would be lexical borrowing, where a word is borrowed from one language into another, such as English job, which was adopted by Germans in the rather specific meaning of temporary occupation. More complex cases involve semantic borrowing, where a way of denoting something is borrowed, not the form itself, such as, for example, the use of the word for mouse to denote a computer mouse in many languages of the world.

Even less well understood are cases where specific aspects of grammar have been transferred. German has, for example, a certain number of neuter nouns, all borrowed from Ancient Greek or Latin, in which the plural is built according to (or inspired by) the Greek model: Lexikon has Lexika as plural, Komma has Kommata as plural, and Kompositum has Komposita as plural. While these cases are spurious in German and thus rather harmless (as are the similar examples in English), there are other cases of language contact where scholars not only suspect that plural forms have been borrowed along with the words (as in German), but that entire paradigms and strategies of grammatical marking have been adopted by one language from a neighboring variety as a result of close language contact.

Why borrowing is hard to detect

Unless we witness them happening directly, most cases of borrowing are difficult to demonstrate consistently. By comparison with lexical borrowing, however, the borrowing of grammar is probably the hardest to show, especially when dealing with abstract categories that could have actually emerged independently. The reason why borrowing is generally hard to deal with, not only in computational approaches, is that detecting borrowing and demonstrating language contact presupposes that alternative explanations are all excluded, such as universal tendencies of language change (i.e., "convergent evolution" in the biological sense), common inheritance, or simple chance.

While we need to exclude alternative possibilities to prove any of the four major types of similarities (coincidental, natural, genealogical, or contact-induced, see List 2014: 55-57), we have a much harder time in doing so when dealing with borrowings, because linguistics does not know even one procedure for the identification of borrowings. Instead, we resort to a mix of different types of evidence, which are qualitatively weighted and discussed by the experts. While historical linguistics has developed sophisticated techniques to show that language similarities are genealogical, it has not succeeded to reach the same level of sophistication for the identification of borrowings.

In this regard, techniques for contact detection are not much different from other, more specific, types of linguistic reconstruction, such as the "philological reconstruction" of ancient pronunciations (Jarceva 1990, Sturtevant 1920), the reconstruction of detailed etymologies (Malkiel 1954), or the reconstruction of syntax (Willis 2011).

Traditional strategies for detecting borrowing

It is not easy to give an exhaustive and clear-cut overview of all of the qualitative methods that scholars make use of in order to detect borrowings among languages. This is at least partially due to the nature of "cumulative-evidence arguments" (Berg 1998) — or arguments based on consilience (Whewell 1840, Wilson 1998) — which are always more difficult to formalize than clear-cut procedures that yield simple, binary results. Despite the difficulty in determining exact workflows, we can identify a couple of proxies that scholars use to assess whether a given trait has been borrowed or not.

One important class of hints are conflicts with possible genealogical explanations. A first type of conflict is represented by similarities shared among unrelated or distantly related languages. Since English mountain is reflected only in English, with similar words only in Romance, we could take this as evidence that the English word was borrowed. Since these conflicts arise from the supposed phylogeny of the languages under consideration, we can speak of phylogeny-related arguments for interference.

A second conflict involves the traits themselves, most prominently observed in the case of irregular sound correspondence patterns. German Damm, for example, is related to English dam, but since the expected correspondence for cognates between English and German would yield a German reflex Tamm (as it is still reflected in Old High German, see Kluge 2002), we can take this as evidence that the modern German term was borrowed (Pfeifer 1993). We can call these cases trait-related arguments for contact.

In addition to observations of conflicts, two further types of evidence are of great importance for inferring contact. The first one is areal proximity, and the second one is the assumed borrowability of traits. Given that language contact requires the direct contact of speakers of different languages, it is self-evident that geographical proximity, including proximity by means of travel routes, is a necessary argument when proposing contact relations between different varieties.

Furthermore, since direct evidence confirms that linguistic interference does not act to the same degree on all levels of linguistic organisation, the notion of borrowability also plays an important role. Although scholars tend to have different opinions about the concept, most would probably agree with the borrowability scale proposed by Aikhenvald (2007, p. 5), which ranges from "inflectional morphology" and "core vocabulary", representing aspects resistant to borrowing, up to "discourse structure" and the "structure of idioms", representing aspects that are easy to borrow. How core vocabulary can be defined, and how the borrowability of individual concepts can be determined and ranked, however, has been subject to controversial discussions (Lee and Sagart 2008, Starostin 1995, Tadmor 2009, Zenner et al. 2014).

Computational strategies for contact inference

Despite the large number of quantitative applications proposed during the past two decades, computational approaches for the inference of contact situations are still in their infancy. As of now, none of the few approaches proposed in the past can compete with the classical methods. The reasons for this are twofold. First, given the multiple types of evidence employed by the classical approaches, the formalization of the problem of borrowing detection is difficult. Second, given the limited number and suitability of datasets annotated for different types of linguistic interference, scholars have a hard time in developing algorithms, since they lack data for testing and training.

In principle, all algorithms for contact inference proposed so far make use of the strategies used in the classical approaches. Thus, they infer or determine shared traits among two or more languages, and then determine conflicts in these traits, taking geographical closeness and borrowability into account. In contrast to classical approaches, which combine different types of evidence, computational approaches are usually restricted to one type.

The automatic methods proposed so far can be divided into three classes. The first class employs phylogeny-related conflicts to identify those traits whose evolution cannot be explained with a given phylogenetic tree, explaining the conflicts as resulting from contact. Examples include work where I was involved myself (Nelson-Sathi et al. 2011, List et al. 2014), some early and interesting approaches which did not receive too much attention (Minett and Wang 2003), or have been mostly forgotten by now (Nakhleh et al. 2005), along with a recent study on grammatical features (Cathcart et al. 2018).

The second class uses techniques for automatic sequence comparison to search for similar words, but not cognate words, across different languages. Here, the most prominent examples include the work by Ark et al. (2007), and later Mennecier et al. (2016), who searched for similar words among languages known to be not related. Further examples include the work by Boc et al. (2010) and Willems et al. (2016), who experimented with tree reconciliation approaches, based on word trees derived from sequence-alignment techniques. There is also an experimental study where I was again involved myself (Hantgan and List forthcoming), in which we tried to identify borrowings by comparing two automatically inferred similarities among words from related and unrelated languages: surface similarities, as reflected by naive alignment algorithms, and deep similarities, reflected by advanced methods that take sound correspondences into account (List 2014).

The third class searches for distribution-related conflicts by comparing the amount of shared words within sublists of differing degrees of borrowability. This class is best represented by Sergey Yakhontov's (1926-2018) work on stable and unstable concept lists (Starostin 1991), which assumed that deep historical relations should surface in those parts of the lexicon that are stable and resistant to borrowing, while recent contact-induced relations would surface rather in those parts of the lexicon that are more prone to borrowing. Yakhontov's work was independently re-invented by Chén (1996), and McMahon et al. (2005); but given how difficult it turned out to distinguish concepts prone to borrowing from those resistant to borrowing, it has been largely disregarded for some time now.

Problems with computational strategies for contact inference

All three classes of approaches discussed so far have certain shortcomings. Phylogeny-based inference of borrowing, for example, tends to drastically overestimate the number of borrowed traits, simply because conflicts in a phylogeny can result from undetected borrowings in the data but they never need to (see Appendix 1 of Morrison 2011 on causes of reticulation in biology, which has many parallels to linguistics). Saying that all instances in which a dataset conflicts with a given phylogeny are borrowings is therefore generally a bad idea. It can be used as a very rough heuristics to come up with potentially wrongly annotated homologies in a dataset, which could then be checked again by experts, but deriving stronger claims from it seems problematic.

While sequence comparison techniques applied to unrelated languages are basically safe in my opinion, and the results are very reliable, unless one compares words that occur in all languages, such as "mama" and "papa" (Jakobson 1960, see also "Mama and papa" on Wikipedia).

Using methods for tree reconciliation on individual word trees, calculated from word distances based on phonetic alignment techniques or similar, yields the same problems of over-counting conflicts as we get for phylogeny-based approaches to borrowing. The problem here is a general misunderstanding of the concept differences between gene trees in biology, where surface similarity of gene sequences is thought to reflect evolutionary history, and word trees in linguistics. While we can use qualitative methods to draw a word tree for a given set of homologous words, the surface similarity among the words says little, if anything, about their evolutionary history.

Attempts to distinguish borrowed from inherited traits with sublists have lost their popularity in most recent studies. When properly applied, they might, indeed, provide some evidence in the search for borrowings or deep homologies. So far, however, all stability rankings of concepts that have been proposed have been based on too small an amount of either concepts (we would need rankings for some 1,000 concepts at least), or languages from which the information was derived. If we could manage to get reliable counts on some 1,000 concepts for a larger sample of the world's languages, this might greatly help our field, as it would provide us with a starting point from which people could search (even qualitatively) for borrowings in their data.

Outlook

Assuming that currently we have no realistic way to operationalize arguments based on consilience, there is no direct hope to have a fully automatic method for detecting borrowings any time soon. By developing promising existing methods further, however, there is a hope that we can learn a lot more about borrowing processes in the world's languages. What is needed here are, of course, the data that we need in order to apply the methods.

In addition to the above-mentioned automatic approaches for borrowing detection, so far, nobody has tried to use trait-related conflicts to infer borrowings. Since these are usually considered to be quite reliable by experts in historical linguistics, it seems inevitable to work in this direction as well, if we want to tackle the problem of consistent automatic detection of borrowing. Here, my recently proposed framework for a consistent handling and identification of patterns of sound correspondences across multiple languages (List 2019), could definitely be useful, although it will again be challenging to find the right balance of parameters and interpretation, since not all conflicts in sound correspondences necessarily result from borrowings.

Whether it will be possible to identify even the direction of borrowings, when developing these methods further, is an open question. Borrowability accounts might help here, but again, since no clear-cut strategies are being used by scholars, it is difficult to formalize any of the existing qualitative approaches. The greatest challenge will perhaps consist in the creation of a database of known borrowings that could assist digital linguists in testing and training new approaches.

References

Aikhenvald, Alexandra Y. (2007) Grammars in contact. A cross-linguistic perspective. In: Aikhenvald, Alexandra Y. and Dixon, Robert M. W. (eds.) Grammars in Contact. Oxford:Oxford University Press. 1-66.

van der Ark, René and Mennecier, Philippe and Nerbonne, John and Manni, Franz (2007) Preliminary identification of language groups and loan words in Central Asia. In: Proceedings of the RANLP Workshop on Acquisition and Management of Multilingual Lexicons, pp. 13-20.

Berg, Thomas (1998) Linguistic Structure and Change: an Explanation from Language Processing. Gloucestershire:Clarendon Press.

Boc, Alix and Di Sciullo, Anna Maria and Makarenkov, Vladimir (2010) Classification of the Indo-European languages using a phylogenetic network approach. In: Locarek-Junge, H. and Weihs, C. (eds.) Classification as a Tool for Research. Berlin and Heidelberg:Springer. 647-655.

Cathcart, Chundra and Carling, Gerd and Larson, Filip and Johansson, Richard and Round, Erich (2018) Areal pressure in grammatical evolution. An Indo-European case study. Diachronica 35.1: 1-34.

Chén Bǎoyà 陈保亚 (1996) Lùn yǔyán jiēchù yǔ yǔyán liánméng 论语言接触与语言联盟 [Language Contact and Language Unions]. Běijīng 北京:Yǔwén 语文.

Hantgan, Abbie and List, Johann-Mattis (forthcoming) Bangime: Secret language, language isolate, or language island? Journal of Language Contact.

Jakobson, Roman (1960): Why 'Mama' and ‘Papa'?. In: Perspectives in Psychological Theory: Essays in Honor of Heinz Werner, pp. 124-134.

Jarceva, V. N. (1990) Lingvistil'eskij enciklopedil'eskij slovar'. Moscow: Sovetskaja Enciklopedija.

Kluge, Friedrich (2002) Etymologisches Wörterbuch der deutschen Sprache. Berlin:de Gruyter.

Lee, Yeon-Ju and Sagart, Laurent (2008) No limits to borrowing: The case of Bai and Chinese. Diachronica 25.3: 357-385.

List, Johann-Mattis and Nelson-Sathi, Shijulal and Geisler, Hans and Martin, William (2014) Networks of lexical borrowing and lateral gene transfer in language and genome evolution. Bioessays 36.2: 141-150.

List, Johann-Mattis (2014) Sequence Comparison in Historical Linguistics. Düsseldorf: Düsseldorf University Press.

List, Johann-Mattis (2019) Automatic inference of sound correspondence patterns across multiple languages. Computational Linguistics 1.45: 137-161.

Malkiel, Yakov (1954): Etymology and the structure of word families. Word 10.2-3: 265-274.

McMahon, April and Heggarty, Paul and McMahon, Robert and Slaska, Natalia (2005) Swadesh sublists and the benefits of borrowing: an Andean case study. Transactions of the Philological Society 103: 147-170.

Phillipe Mennecier and John Nerbonne and Evelyne Heyer and Franz Manni (2016) A Central Asian language survey. Language Dynamics and Change 6.1: 57–98.

Minett, James W. and Wang, William S.-Y. (2003) On detecting borrowing. Diachronica 20.2: 289–330.

Morrison, D. A. (2011) An Introduction to Phylogenetic Networks. Uppsala: RJR Productions.

Nakhleh, Luay and Ringe, Don and Warnow, Tandy (2005) Perfect Phylogenetic Networks: a new methodology for reconstructing the evolutionary history of natural languages. Language 81.2: 382-420.

Nelson-Sathi, Shijulal and List, Johann-Mattis and Geisler, Hans and Fangerau, Heiner and Gray, Russell D. and Martin, William and Dagan, Tal (2011) Networks uncover hidden lexical borrowing in Indo-European language evolution. Proceedings of the Royal Society of London B: Biological Sciences 278.1713: 1794-1803.

Pfeifer, Wolfgang (1993) Etymologisches Wörterbuch des Deutschen. Berlin: Akademie.

Starostin, Sergej Anatolévic (1991) Altajskaja problema i proischoždenije japonskogo jazyka [The Altaic Problem and the Origin of the Japanese Language]. Moscow: Nauka.

Starostin, Sergej Anatolévic (1995) Old Chinese vocabulary: A historical perspective. In: Wang, William S.-Y. (ed.) The Ancestry of the Chinese Language. Berkeley: University of California Press, pp. 225-251.

Sturtevant, Edgar H. (1920) The Pronunciation of Greek and Latin. Chicago: University of Chicago Press.

Tadmor, Uri (2009): Loanwords in the world’s languages. Findings and results. In: Haspelmath, Martin and Tadmor, Uri (eds.) Loanwords in the World's Languages. Berlin and New York: de Gruyter, pp. 55-75.

Whewell, William D. D. (1847) The Philosophy of the Inductive Sciences, Founded Upon Their History. London: John W. Parker.

Willems, Matthieu and Lord, Etienne and Laforest, Louise and Labelle, Gilbert and Lapointe, François-Joseph and Di Sciullo, Anna Maria and Makarenkov, Vladimir (2016) Using hybridization networks to retrace the evolution of Indo-European languages. BMC Evolutionary Biology 16.1: 1-18.

David Willis (2011) Reconstructing last week's weather: Syntactic reconstruction and Brythonic free relatives. Journal of Linguistics 47.2: 407-446.

Wilson, Edward O. (1998) Consilience: the Unity of Knowledge. New York: Vintage Books.

Zenner, Eline and Dirk Speelman and Dirk Geeraerts (2014) Core vocabulary, borrowability and entrenchment. Diachronica 31.1: 74–105.