Inducing nonlocal constraints from baseline phonotactics

Abstract

Nonlocal phonological patterns such as vowel harmony and long-distance consonant assimilation and dissimilation motivate representations that include only the interacting segments—projections. We present an implemented computational learner that induces projections based on phonotactic properties of a language that are observable without nonlocal representations. The learner builds on the base grammar induced by the MaxEnt Phonotactic Learner (Hayes and Wilson 2008). Our model searches this baseline grammar for constraints that suggest nonlocal interactions, capitalizing on the observations that (a) nonlocal interactions can be seen in trigrams if the language has simple syllable structure, and (b) nonlocally interacting segments define a natural class. We show that this model finds nonlocal restrictions on laryngeal consonants in corpora of Quechua and Aymara, and vowel co-occurrence restrictions in Shona.

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Notes

  1. 1.

    An anonymous reviewer asks how crucial it is to assume that the segmental inventory is given in advance. This is an interesting question, since traditional phonological reasoning about analyzing segmental inventories does usually depend on phonotactics: for example, the analysis of English [ʧ] as an affricate and [ts] as a cluster relies on distributional information. We do not attempt to solve this complex problem here, though see Sect. 5 for some related discussion.

  2. 2.

    Della Pietra et al. (1997:4) characterize gain as “the improvement [a constraint] brings to the model when it has weight [w]”: \(\mathit{Gain}_{Con}(w,C)=D(\tilde{p}||\mathit{Con})-D(\tilde{p}||\mathit{Con}_{wC})\), where C is the constraint with the weight w, D is the Kullback-Leibler divergence, is the probability distribution of the data, and Con is the current constraint grammar.

    Della Pietra et al. explain the reason for this method of calculating gain intuitively as follows: “We approximate the improvement due to adding a single candidate [constraint], measured by the reduction in Kullback-Leibler divergence, by adjusting only the weight of the [constraint] and keeping all of the other parameters of the [grammar] fixed. In general this is only an estimate, since it may well be that adding a [constraint] will require significant adjustments to all of the parameters in the new model. From a computational perspective, approximating the improvement in this way can enable the simultaneous evaluation of thousands of candidate [constraints], and makes the algorithm practical.” (We modified the language slightly to translate it into constraint/grammar terms.) We might add that defined in this way, gain can be calculated for each constraint even when the grammar contains no constraints yet, whereas for O/E, there needs to be an arbitrarily set threshold.

  3. 3.

    We did the counts for a transcribed Russian dictionary of 103,000 words. Looking at consonants in trigram and tetragram configurations, CVC accounted for 337,415 or 63% of all the combinations; CCC: 18,516 (3.4%), CCVC: 76,574 (14%), CVCC: 93,637 (18%), CVVC: 7,946 (1%). For vowel-to-vowel n-grams, the counts are VCV: 117,214 (64%), VCCV: 61,344 (33%), VCVV: 2,074 (1%), VVCV: 2512 (1%). We give comparable numbers for other languages, where relevant, in their respective sections.

  4. 4.

    Padgett (1991) does report a gradient co-occurrence restriction in 500 Russian roots; see also Kochetov and Radisic (2009).

  5. 5.

    Aspirates may also appear in vowel-initial words, though ejectives are absent from such forms. See Gallagher (2015) for discussion.

  6. 6.

    Our corpus is available on GitHub at https://github.com/gouskova/inductive_projection_learner. While the newspaper is primarily a Quechua language periodical, it includes numerous articles in Spanish, as well as Spanish phrases and Spanish roots embedded in Quechua text. The majority of Spanish forms were removed from the word corpus, including Spanish words that were inflected with Quechua morphology. The only exception to this are those words, mostly place names, that are consistent with the native phonotactics of Quechua.

  7. 7.

    Another parameter is whether the model is asked to look for violable or inviolable constraints. In either condition, whether a constraint is included in the grammar depends on its gain, but an inviolable constraint simulation only considers constraints whose observed violations are zero. To keep the amount of information digestible, we only consider inviolable constraint models of Quechua and Aymara, since the laryngeal phonotactics are categorical. The results reported here are replicable with similar settings for violable constraint models as well. For all models reported throughout this paper, we ran the model with a large enough constraint set that the model returned fewer constraints than it was asked for. This means that constraint set size was not an analyst-manipulated parameter that affected the fit of the model.

  8. 8.

    A baseline grammar run on a modified Quechua training set where codas were added to all syllables confirmed that this is true; the grammar includes a highly weighted constraint against stop-consonant bigrams, but no trigram constraints on stop-[ ]-ejective or stop-[ ]-aspirate sequences.

  9. 9.

    Indeed, a model where binary [sg] and [cg] are used does not include any constraints on plain stops. This could be interpreted as a failing of the heuristic in the Hayes and Wilson model, or it could be taken as evidence that privative features are a better hypothesis in this particular case.

  10. 10.

    This means that the phonotactic learning here happens over a sublexicon of roots; see Sect. 6.5 for more discussion.

  11. 11.

    The use of a placeholder segment ‘X’ is of course not the ideal solution to this problem, as it obscures any other phonological generalizations that may hold of segments that are in an identity relation, like local restrictions on clusters of consonant-vowel interactions. A superior model would expand the search space of constraint to include algebraic notation. While Berent et al. (2012) present one potential method for constructing constraints of this type, no implementation of the model in that paper is available, nor has it been shown to be a general solution to phonological distinctions between identical and non-identical segments.

  12. 12.

    Morphologically, most of these stems appear to be imperatives, which are roots followed by some verbal projection suffixes (causatives, applicatives, etc.) and the [-a] suffix. Since all the citation forms of verbs end in [-a], this throws off the calculations for sequences that end in [a], so we removed that suffix for the purposes of O/E calculations. The suffix is present in the learning data for the simulations we report, however, since it is a categorical fact about Shona phonotactics that all words end in vowels.

  13. 13.

    We opted to use a different corpus from Hayes and Wilson (2008), who used an incomplete scanned version (Hannan 1974) that goes up to “m”. Our corpus is slightly smaller but contains the full range of initial consonants, which matters for phonotactic learning. We verified that the distribution of vowel-vowel pairs is comparable in the two corpora.

  14. 14.

    Suffixes harmonize with verbal roots, but Fortune mentions a minor pattern whereby root vowels alternate to match the final -a or -e: [ndi-ger-e] ‘I am seated’ vs. [ku-gar-a], [ndi-ɲerer-e] ‘I am silent’ vs. [ku-ɲarar-a]. He lists five roots that follow this pattern; all alternate between [a] and [e] (Fortune 1980:20). We leave the phonological analysis of this for future work; for our present purposes, the important observation is that even the minor alternations are consistent with the phonotactic characterization of vowel harmony that affixes display.

  15. 15.

    The list of clusters we included: [gw, mw, bw, hw, kw, sw, nd, ŋg, mb, nz, nʤ].

  16. 16.

    The exhaustive list of clusters that occur in the Shona corpus: [ʦw, kw, âw, rw, mv, ʃw, zw, nw, tw, ŋw, jw, mw, w, sw, hw, pw, Ʒw, , gw, ɬw, nâw, mb, nâ, ŋg, nz, nɮ, ɲŋ, nj, âr, mbw, nhw, jŋ, ŋgw, nzw, nzv]. Many of these could be analyzed as labialized singletons or prenasalized stops or fricatives. The attractiveness of this move is somewhat tempered by the computational cost of increasing the number of natural classes. We do not know of a phonological analysis that would allow treating sequences such as [nj] or [âr] as singletons.

  17. 17.

    Technically, [-low] includes [e i o u j w], since we specified the glides in the feature set as [-syll] segments with vocalic features. When the feature set was rigged to exclude glides from vowel natural classes, the results did not change.

  18. 18.

    The details of this statistical analysis are provided along with the code for the learner on GitHub (https://github.com/gouskova/inductive_projection_learner). In both the baseline and the final grammar, VCCV forms receive slightly higher harmony scores than VCV forms. Since the constraints on CC sequences are poorly understood, we severely limited the range of clusters in our nonce words. This means that VCV forms, with their wider range of consonants in medial position, are more likely to violate bigram constraints on CV and VC sequences. We do not know what the status of these constraints is in Shona speakers’ grammars, so it is an open question whether the computational learner is overfitting.

  19. 19.

    An anonymous reviewer suggests evaluating the fit of the [+syllabic] phonotactic grammar with that of our mosaic grammar in a linear model, as we did for the baseline vs. mosaic grammars earlier. Unsurprisingly, given the visual impression in the plot, there is a significant effect of vowel harmony status on harmony scores in a linear model for the [+syllabic] grammar. The question, then, is whether it is possible to decide which model is better on the basis of such statistical comparisons. The usual methods of model comparison such as Akaike Information Criterion do distinguish these models, favoring [+syllabic] over the mosaic model (52,773 vs. 53,140—lower is better)—but this comparison also favors the baseline model (AIC = 49,724) over both of the models that capture the vowel harmony generalizations that we are after. The statistical method of evaluating models therefore points away from linguistic intuitions, which could be a potential problem for us. The only way to find out which model captures the right generalizations is to test them experimentally on human speakers of Shona.

  20. 20.

    A similar criticism can be applied to the model of Goldsmith and Riggle (2012). They argue that their model discovers the projection relevant to Finnish vowel harmony, but it does so over segmental rather than featural representations—thus, the comparison is between V-to-V vs. V-to-C nonlocal relations. This assumes that the learner is considering only V and C natural classes, thereby giving the learner a vocalic projection for free. It also allows the learner to notice accidental nonlocal co-occurrence restrictions that do not involve segments from the same natural class, which our learner cannot detect.

  21. 21.

    Russian is one of the languages that causes the Java implementation of the learner to run out of memory at the constraint enumeration stage, due to the large number of natural classes. We got around this for Russian by redefining the feature set to use several privative oppositions and not transcribing certain important phonotactic patterns (such as vowel reduction). This reduces the number of natural classes for the learner to deal with, and with it the ability to make certain phonological generalizations. Even this move did not help with Hungarian.

  22. 22.

    An anonymous reviewer asks why nonlocal interactions aren’t more frequent in Polynesian languages, which have very simple syllable structure. First, several languages of the region have been noted for their nonlocal consonant interactions (see Blust 2012 for a review of OCP effects in these languages, as well as Coetzee and Pater 2008; Zuraw and Lu 2009). While we do predict that nonlocal interactions should be learnable via our method in Polynesian languages, there may be other reasons, including chance, why a language does or does not exhibit a particular type of pattern. For example, in a language with a small segmental inventory and simple syllable structure, nonlocal phonological dependencies introduce additional limitations on possible words, resulting in a relatively small set of unique words, unless words are extremely long. Morphological reduplication may make phonotactic nonlocal dependencies difficult to detect, since patterns may be ambiguous between a phonotactic and a morphological analysis.

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Acknowledgements

For helpful feedback, we would like to thank Arto Anttila and the anonymous reviewers of NLLT, as well as Maddie Gilbert, Juliet Stanton, Ildi Emese Szabó, Sora Heng Yin, Jon Rawski, audiences at OCP 2018 in London, UMass Amherst, Stony Brook, and the Phonology Winter School in Israel. Finally, we would like to thank Daniel Ridings for making the ALLEX corpus wordlist available to us, and Colin Wilson for sharing the code for the gain-based MaxEnt Phonotactic Learner, as well as detailed feedback on related work. This research was supported in part by NSF BCS-1724753 to the authors.

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Gouskova, M., Gallagher, G. Inducing nonlocal constraints from baseline phonotactics. Nat Lang Linguist Theory 38, 77–116 (2020). https://doi.org/10.1007/s11049-019-09446-x

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Keywords

  • Phonology
  • Phonotactics
  • Computational modeling
  • Inductive learning
  • Learnability
  • Consonant harmony
  • Consonant dissimilation
  • Vowel harmony
  • Nonlocal phonology
  • Corpus phonology
  • Quechua
  • Aymara
  • Shona