Bar-Sever, Galia

Lee, Rachael

Pearl, Lisa

Scontras, Gregory

English

ScholarWorks@UMass Amherst

Proceedings of the Society for Computation in LinguisticsVolume 1 Article 272018Quantitatively Assessing the Development ofAdjective Ordering Preferences Using Child-directed and Child-produced Speech CorporaGalia Bar-SeverUniversity of California, Irvine, gbarsever@gmail.comRachael LeeUniversity of California, Irvine, rachaejl@uci.eduGregory ScontrasUniversity of California, Irvine, gscontra@uci.eduLisa PearlUniversity of California, Irvine, lpearl@uci.eduFollow this and additional works at: https://scholarworks.umass.edu/scilPart of the Computational Linguistics CommonsThis Abstract is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Proceedings of theSociety for Computation in Linguistics by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contactscholarworks@library.umass.edu.Recommended CitationBar-Sever, Galia; Lee, Rachael; Scontras, Gregory; and Pearl, Lisa (2018) "Quantitatively Assessing the Development of AdjectiveOrdering Preferences Using Child-directed and Child-produced Speech Corpora," Proceedings of the Society for Computation inLinguistics: Vol. 1 , Article 27.DOI: https://doi.org/10.7275/R5QR4V9JAvailable at: https://scholarworks.umass.edu/scil/vol1/iss1/27Quantitatively assessing the development of adjective ordering preferencesusing child-directed and child-produced speech corporaGalia Bar-Sever, Rachael Lee, Gregory Scontras, Lisa PearlUniversity of California, IrvineAdults have robust ordering preferences that determine the relative order of adjectives in multi-adjectivestrings: this is why “small gray kitten” is preferable to “gray small kitten” in English and many other un-related languages. These preferences appear to be based on abstract representations, rather than simplyreflecting where specific adjectives appear in the input. One hypothesis holds that adjective ordering isdetermined by abstract syntax, with adjectives grouped into lexical semantic classes that are hierarchicallyordered (Dixon, 1982; Cinque, 1994). These lexical classes and their hierarchical ordering are then prim-itives in the representation of the preferences. Recently, Scontras, Degen, and Goodman (2017) identifiedadjective subjectivity as a robust predictor of ordering preferences, with less subjective adjectives preferredcloser to the modified noun; they advanced the hypothesis that ordering preferences—and the lexical classordering observed cross-linguistically—derive from the perceived subjectivity of the adjectives. Despite thecross-linguistic robustness of these ordering preferences, little is known about their development in children,other than that these preferences do in fact develop (Bever, 1970; Martin & Molfese, 1972; Hare & Otto,1978). To assess when more abstract knowledge about adjective ordering emerges and how that knowledgegets represented, we use corpus analysis and quantitative metrics connecting children’s input, underlyingrepresentations, and output. We find that a more abstract representation does not emerge until four yearsold, and this representation appears to be based on ordered lexical classes rather than subjectivity.To assess children’s input and children’s output, we examined 688,428 child-directed and 1,069,406child-produced utterances from ages 2 to 4 in CHILDES (MacWhinney, 2000). This dataset yielded 3,066adjective-adjective-noun (AdjAdjN) strings in child-directed speech (6,132 adjective tokens of 383 types)and 975 AdjAdjN strings in child-produced speech (1,986 adjective tokens of 232 types). We compared threeunderlying representations connecting children’s input to their output: (i) input frequency of the adjective’sposition in AdjAdjN strings, (ii) hierarchically ordered lexical semantic classes, and (iii) subjectivity-basedordering. Each adjective from the AdjAdjN datasets was assigned to a lexical semantic class and associatedwith an adult subjectivity score. We obtained these subjectivity scores from 108 adult participants on MTurk,replicating the methodology of Scontras et al. (2017) for child-register adjectives like teeny.To compare the input frequency vs. lexical semantic class vs. subjectivity hypotheses at ages 2, 3, and 4,we calculated the likelihood of the age-specific child-produced AdjAdjN data under each hypothesis, giventhe child-directed input (see equations (1) – (4)). Log likelihood scores for each representation at each ageappear in Table 1. We find that input frequency—in other words, simply tracking the word-level positionstatistics—best accounts for the child AdjAdjN productions at ages 2 and 3, while the lexical semantic classhypothesis best accounts for child AdjAdjN productions at age 4. This finding suggests that more abstractknowledge underlies children’s adjective ordering preferences at age 4 (but not earlier), and that this ab-stract knowledge is lexical-class-based rather than subjectivity-based. We see the emergence of this abstractknowledge by observing the difference between the lexical class and input frequency hypotheses in terms ofdata coverage: from age 2 to 3, the lexical class score approaches input frequency’s and then overtakes it byage 4. The subjectivity hypotheses has a similar pattern: the difference between it and the best performinghypothesis narrows as children age, though subjectivity never overtakes the winning hypothesis through age4. Thus, it remains unclear when (or whether) subjectivity replaces lexical class as the underlying repre-sentation for adjective ordering preferences—this may depend on children’s development of the conceptualunderpinnings of subjectivity, which occurs remarkably late (Foushee & Srinivasan, 2017). Taken together,our results demonstrate that children initially track the word-level statistics of their input when determiningadjective ordering preferences. By age 4, they shift to a more abstract and compact representation based onlexical semantic class.203Proceedings of the Society for Computation in Linguistics (SCiL) 2018, pages 203-204.Salt Lake City, Utah, January 4-7, 2018Equations for relating underlying representations to observed adjective position in productions:p2exp(adjx) =finput(< adjx) + 0.5 ∗ finput(= adjx)Ninput(adj)(1)Equation 1: Probability of adjx appearing two positions away from the noun (2-away) when combinedwith another adjective, given the lexical class or subjectivity hypothesis. finput(< adjx) is the numberof adjective tokens that are from a closer lexical class or are less subjective, depending on the hypothesis.finput(= adjx) is the number of adjectives in the same lexical class or with equal subjectivity; adjx willappear 2-away with a 50% chance in this case. Ninput(adj) is the number of adjective tokens that appearedin AdjAdjN strings in the input.p2exp(adjx) =f2input(adjx)Ninput(adjx)(2)Equation 2: Probability of adjx appearing 2-away as dictated by its position in the input. f2input(adjx) is thecount of adjx appearing 2-away; Ninput(adjx) is the total number of AdjAdjN strings where adjx appeared.p(D(adjx)|H) =(Nf)(p2exp(adjx))f (1− p2exp(adjx))N−f (3)Equation 3: The likelihood of the data for a given adjective D(adjx) under a specific representationalhypothesis H . This depends on the number of total times that adjective appeared in an AdjAdjN string (N ),the number of times that adjective appeared in the 2-away position (f ), and the probability of the adjectiveappearing in the 2-away position (p2exp(adjx)).p(D|H) =∏adjx∈Ap(D(adjx)|H) (4)Equation 4: The total likelihood of the outputdata D under H is the product of the individ-ual adjective likelihood probabilities.representational hypothesesage input freq. lexical class subjectivity2 -202.6 -334.9 -322.43 -125.1 -164.0 -187.44 -182.9 -165.2 -221.0Table 1: Log likelihood scores for each hypothesis. Scoresrange from 0 (best) to -infinity (worst). The best score foreach age is bolded.ReferencesBever, T. G. (1970). The cognitive basis for linguistic structures. Cognition and the development of language.Cinque, G. (1994). On the evidence for partial N-movement in the Romance DP. In R. S. Kayne, G. Cinque, J. Koster,J.-Y. Pollock, L. Rizzi, & R. Zanuttini (Eds.), Paths Towards Universal Grammar. Studies in Honor of RichardS. Kayne (pp. 85–110). Washington DC: Georgetown University Press.Dixon, R. M. (1982). Where have all the adjectives gone? and other essays in semantics and syntax (Vol. 107). Walterde Gruyter.Foushee, R., & Srinivasan, M. (2017). Could both be right? Children’s and adults’ sensitivity to subjectivity inlanguage. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society (p. 379-3384). London,UK: Cognitive Science Society.Hare, V. C., & Otto, W. (1978). Development of preferred adjective ordering in children, grades one through five. TheJournal of Educational Research, 190–193.MacWhinney, B. (2000). The CHILDES project: The database (Vol. 2). Psychology Press.Martin, J. E., & Molfese, D. L. (1972). Preferred adjective ordering in very young children. Journal of Verbal Learningand Verbal Behavior, 11(3), 287–292.Scontras, G., Degen, J., & Goodman, N. D. (2017). Subjectivity predicts adjective ordering preferences. Open Mind:Discoveries in Cognitive Science, 1, 53-65.204

Quantitatively Assessing the Development of Adjective Ordering Preferences Using Child-directed and Child-produced Speech Corpora

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Quantitatively Assessing the Development of Adjective Ordering Preferences Using Child-directed and Child-produced Speech Corpora

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