Word Order Typology Interacts With Linguistic Complexity: A Cross-Linguistic Corpus Study.
Corpus linguistics
Dependency treebanks
Language adaptation
Language typology
Quantitative linguistics
Working memory constraints
Journal
Cognitive science
ISSN: 1551-6709
Titre abrégé: Cogn Sci
Pays: United States
ID NLM: 7708195
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
15
08
2018
revised:
26
12
2019
accepted:
17
01
2020
entrez:
31
3
2020
pubmed:
31
3
2020
medline:
28
8
2021
Statut:
ppublish
Résumé
Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008, 2015; Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross-linguistically (Ferrer-i Cancho, 2014; Futrell, Mahowald, & Gibson, 2015; Liu, Xu, & Liang, 2017). This implies that on average languages avoid inefficient or complex structures for simpler structures. But a number of studies in psycholinguistics (Konieczny, 2000; Levy & Keller, 2013; Vasishth, Suckow, Lewis, & Kern, 2010) show that the comprehension system can adapt to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject-Verb-Object (SVO) or Subject-Object-Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the direction of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results show that most languages tend to avoid greater structural depth. Together, these results show evidence for "limited adaptability" to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e12822Informations de copyright
© 2020 Cognitive Science Society, Inc.
Références
Altmann, G. T. (1999). Thematic role assignment in context. Journal of Memory and Language, 41(1), 124-145.
Altmann, G. T., & Kamide, Y. (1999). Incremental interpretation at verbs: Restricting the domain of subsequent reference. Cognition, 73(3), 247-264.
Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Lawrence Erlbaum Associates.
Apurva, & Husain, S. (2018). Working memory constraints override prediction in processing center embedded constructions. In P. Knoeferle (Ed.), Proceedings of the architectures and mechanisms of language processing-Asia (AMLaP-Asia). Berlin: Humboldt-Universität zu Berlin Germany.
Apurva, & Husain, S. (2019). Local coherence and case-marker exchange cause parsing errors in Hindi. In P. Baths (Ed.), Proceedings of the sixth annual conference of the Association for Cognitive Science in India (ACCS) (pp. 39-43). Goa, India.
Bach, E., Brown, C., & Marslen-Wilson, W. (1986). Crossed and nested dependencies in German and Dutch: A psycholinguistic study. Language and Cognitive Processes, 1(4), 249-262.
Baddeley, A., & Hitch, J. (1974). Working memory. In G. Bower (Ed.), Recent advances in learning and motivation (Vol. 8, pp. 47-89). New York: Academic Press.
Bartek, B., Lewis, R. L., Vasishth, S., & Smith, M. (2011). In search of on-line locality effects in sentence comprehension. Journal of Experimental Psychology: Learning, Memory and Cognition, 37(5), 1178-1198.
Bates, D., & Sarkar, D. (2007). lme4: Linear mixed-effects models using s4 classes [Computer software manual]. R package version 0.9975-11.
Behagel, O. (1932). Deutsche syntax: Eine geschichtliche darstellung, wortstellung. periodenbau. (Vol. iv). Heidelberg: Carl Winters.
Bhatia, S., & Husain, S. (2018). Forgetting effects due to local coherence in Hindi. In F. Ferreira et al. (Eds.), Proceedings of the 31st Annual CUNY Sentence Processing Conference. Davis, CA: UC Davis.
Bhatia, S., & Husain, S. (2019). Prediction failure and local coherence in a head-final language. In Proceedings of the psycholinguistics in Iceland - Parsing and prediction (PIPP) conference, Reykjavik.
Boland, J. E., Tanenhaus, M. K., Garnsey, S. M., & Carlson, G. N. (1995). Verb argument structure in parsing and interpretation: Evidence from wh-questions. Journal of Memory and Language, 34(6), 774-806.
Boston, M. F., Hale, J., Kliegl, R., Patil, U., & Vasishth, S. (2008). Parsing costs as predictors of reading difficulty: An evaluation using the potsdam sentence corpus. Journal of Eye Movement Research, 2(1), 1-12.
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using stan. Journal of Statistical Software, 80(1), 1-28.
Campanelli, L., Van Dyke, J. A., & Marton, K. (2018). The modulatory effect of expectations on memory retrieval during sentence comprehension. In T. T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th annual conference of the Cognitive Science Society (pp. 1434-1439). Austin, TX: Cognitive Science Society.
Chang, F. (2009). Learning to order words: A connectionist model of heavy NP shift and accessibility effects in Japanese and English. Journal of Memory and Language, 61(3), 374-397.
Chang, F. (2015). The role of learning in theories of English and Japanese processing. In M. Nakayama (Ed.), Handbook of Japanese psycholinguistics (pp. 353-386). Berlin: De Gruyter.
Chen, E., Gibson, E., & Wolf, F. (2005). Online syntactic storage costs in sentence comprehension. Journal of Memory and Language, 52(1), 144-169.
Chomsky, N., & Miller, G. A. (1963). Introduction to the formal analysis of natural languages. In R. Duncan Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of mathematical psychology (pp. 269-321). New York: Wiley.
Christiansen, M. H., & Chater, N. (1999). Toward a connectionist model of recursion in human linguistic performance. Cognitive Science, 23, 157-205.
Christiansen, M. H., & MacDonald, M. C. (2009). A usage-based approach to recursion in sentence processing. Language Learning, 59(S1), 126-161.
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87-114.
Daily, L. Z., Lovett, M. C., & Reder, L. M. (2001). Modeling individual differences in working memory performance: A source activation account. Cognitive Science, 25(3), 315-353.
Demberg, V., & Keller, F. (2008). Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition, 109(2), 193-210.
Demberg, V., Keller, F., & Koller, A. (2013). Incremental, predictive parsing with psycholinguistically motivated tree-adjoining grammar. Computational Linguistics, 39(4), 1025-1066.
Dryer, M. S. (1992). The Greenbergian word order correlations. Language, 68, 81-138.
Dyke, J. A. V., & Johns, C. L. (2012). Memory interference as a determinant of language comprehension. Language and Linguistics Compass, 6(4), 193-211.
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 213-252.
Engelmann, F., & Vasishth, S. (2009). Processing grammatical and ungrammatical center embeddings in English and German: A computational model. In A. Howes, D. Peebles, & R. Cooper (Eds.), Proceedings of 9th international conference on cognitive modeling (pp. 240-245). Manchester, UK.
Ferreira, F. (1991). Effects of length and syntactic complexity on initiation times for prepared utterances. Journal of Memory and Language, 20, 210-233.
Ferrer-i Cancho, R. (2006). Why do syntactic links not cross? Europhysics Letters (EPL), 76(6), 1228-1235.
Ferrer-i Cancho, R. (2008). Some word order biases from limited brain resources: A mathematical approach. Advances in Complex Systems, 11(03), 393-414.
Ferrer-i Cancho, R. (2014). Why might sov be initially preferred and then lost or recovered? A theoretical framework. In E. Carthill et al. (Eds.), Evolution of language: Proceedings of the 10th international conference (evolang10) (pp. 66-73). Singapore: World Scientific.
Ferrer-i Cancho, R. (2015). The placement of the head that minimizes online memory. Language Dynamics and Change, 5(1), 114-137.
Ferrer-i-Cancho, R. (2017). The placement of the head that maximizes predictability. An information theoretic approach. Glottometrics, 39, 38-71.
Ford, M. (1983). A method for obtaining measures of local parsing complexity throughout sentences. Journal of Verbal Learning and Verbal Behavior, 22(2), 203-218.
Frank, S. L., & Bod, R. (2011). Insensitivity of the human sentence-processing system to hierarchical structure. Psychological Science, 22(6), 829-834.
Frank, S. L., & Ernst, P. (2019). Judgements about double-embedded relative clauses differ between languages. Psychological Research, 83, 1581-1593.
Frank, S. L., Trompenaars, T., & Vasishth, S. (2016). Cross-linguistic differences in processing double-embedded relative clauses: Working-memory constraints or language statistics? Cognitive Science, 40(3), 554-578.
Frazier, L. (1985). Syntactic complexity. In L. K. D. Dowty & A. Zwicky (Eds.), Natural language parsing (pp. 129-189). Cambridge: Cambridge University Press.
Futrell, R., Gibson, E., & Levy, R. (2019). Lossy-context surprisal: An information-theoretic model of memory effects in sentence processing. Cognitive Science, 44, e12814. https://doi.org/10.1111/cogs.12814
Futrell, R., & Levy, R. (2017). Noisy-context surprisal as a human sentence processing cost model. In M. Lapata et al. (Eds.), Proceedings of the 15th conference of the European chapter of the Association for Computational Linguistics (EACL) (pp. 688-698). Valenia, Spain: Association for Computational Linguistics.
Futrell, R., Mahowald, K., & Gibson, E. (2015). Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences, 112(33), 10336-10341.
Gibson, E. (1991). A computational theory of human linguistic processing: Memory limitations and processing breakdown. Unpublished doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.
Gibson, E. (1998). Linguistic complexity: Locality of syntactic dependencies. Cognition, 68(1), 1-76.
Gibson, E. (2000). The dependency locality theory: A distance-based theory of linguistic complexity. In A. Marantz, Y. Miyashita, & W. O'Neil (Eds.), Image, language, brain (pp. 95-126). Cambridge, MA: MIT Press.
Gibson, E., & Thomas, J. (1999). Memory limitations and structural forgetting: The perception of complex ungrammatical sentences as grammatical. Language and Cognitive Processes, 14(3), 225-248.
Gimenes, M., Rigalleau, F., & Gaonac'h, D. (2009). When a missing verb makes a French sentence more acceptable. Language and Cognitive Processes, 24(3), 440-449.
Gómez-Rodríguez, C., Weir, D., & Carroll, J. (2009). Parsing mildly non-projective dependency structures. In A. Lascarides et al. (Eds.), Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics (pp. 291-299). Stroudsburg, PA: Association for Computational Linguistics.
Grodner, D., & Gibson, E. (2005). Consequences of the serial nature of linguistic input. Cognitive Science, 29, 261-290.
Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics (pp. 1-8). Stroudsbutg, PA: ACL.
Häussler, J., & Bader, M. (2015). An interference account of the missing-VP effect. Frontiers in Psychology, 6, 766.
Hawkins, J. A. (1983). Word order universals. New York: Academic Press.
Hawkins, J. A. (1992). Syntactic weight versus information structure in word order variation. In J. Jacobs (Ed.), Informationsstruktur und grammatik (pp. 196-219). Wiesbaden: Springer.
Hawkins, J. A. (1994). A performance theory of order and constituency (Vol. 73). Cambridge: Cambridge University Press.
Hawkins, J. A. (2014). Cross-linguistic variation and efficiency. Oxford, UK: Oxford University Press.
Hsiao, Y., & MacDonald, M. C. (2013). Experience and generalization in a connectionist model of Mandarin Chinese relative clause processing. Frontiers in Psychology, 4, 767.
Hudson, R. (2010). An introduction to word grammar. New York: Cambridge University Press.
Husain, S., & Bhatia, S. (2018). (not) forgetting verbs in Hindi doubly center-embedded structures. In P. Knoeferle (Ed.), In Architectures and mechanisms of language processing-AMLaP. Berlin: Humboldt-Universität zu Berlin Germany.
Husain, S., & Vasishth, S. (2014). Reactivation effects interact with expectation strength. In Proceedings of the architectures and mechanisms of language processing (AMLAP).
Husain, S., & Vasishth, S. (2015). Non-projectivity and processing constraints: Insights from Hindi. In J. Nivre & E. Hajičová (Eds.), Proceedings of the third international conference on dependency linguistics (depling 2015) (pp. 141-150). Uppsala: Uppsala University.
Husain, S., Vasishth, S., & Srinivasan, N. (2014). Strong expectations cancel locality effects: Evidence from Hindi. PLoS ONE, 9(7), e100986.
Husain, S., Vasishth, S., & Srinivasan, N. (2015). Integration and prediction difficulty in Hindi sentence comprehension: Evidence from an eye-tracking corpus. Journal of Eye Movement Research, 8(2), 1-12.
Jing, Y., & Liu, H. (2015). Mean hierarchical distance augmenting mean dependency distance. In J. Nivre & E. Hajičová (Eds.), Proceedings of the third international conference on dependency linguistics (depling 2015) (pp. 161-170). Uppsala, Sweden: Uppsala University.
Joshi, A. K. (1985). Tree adjoining grammars: How much context-sensitivity is required to provide reasonable structural descriptions? In D. Dowty, L. Karttunen, & A. Zwicky (Eds.). Natural language parsing (pp. 206-250). Cambridge: Cambridge University Press.
Kimball, J. (1973). Seven principles of surface structure parsing in natural language. Cognition, 2(1), 15-47.
Konieczny, L. (2000). Locality and parsing complexity. Journal of Psycholinguistic Research, 29(6), 627-645.
Kübler, S., McDonald, R., & Nivre, J. (2009). Dependency parsing. San Rafael, CA: Morgan & Claypool.
Kuhlmann, M., & Möhl, M. (2007). Mildly context-sensitive dependency languages. In A. Zaenen & A. van den Bosch (Eds.), Proceedings of the 45th annual meeting of the Association of Computational Linguistics (pp. 160-167). Prague: Association for Computational Linguistics.
Kuhlmann, M., & Satta, G. (2009). Treebank grammar techniques for non-projective dependency parsing. In A. Lascarides et al. (Eds.), Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics (pp. 478-486). Athens: ACL.
Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106, 1126-1177.
Levy, R. (2013). Memory and surprisal in human sentence comprehension. In R. P. G. van Gompel (Ed.), Sentence processing (pp. 78-114). London: Psychology Press.
Levy, R., Fedorenko, E., Breen, M., & Gibson, E. (2012). The processing of extraposed structures in English. Cognition, 122(1), 12-36.
Levy, R., Fedorenko, E., & Gibson, E. (2013). The syntactic complexity of Russian relative clauses. Journal of Memory and Language, 69(4), 461-495.
Levy, R., & Keller, F. (2013). Expectation and locality effects in German verb-final structures. Journal of Memory and Language, 68(2), 199-222.
Lewis, R. L., & Vasishth, S. (2005). An activation-based model of sentence processing as skilled memory retrieval. Cognitive Science, 29(3), 375-419.
Liu, H. (2008). Dependency distance as a metric of language comprehension difficulty. Journal of Cognitive Science, 9(2), 159-191.
Liu, H., Xu, C., & Liang, J. (2017). Dependency distance: A new perspective on syntactic patterns in natural languages. Physics of Life Reviews, 21, 171-193.
Lovett, M. C., Daily, L. Z., & Reder, L. M. (2000). A source activation theory of working memory: Cross-task prediction of performance in ACT-R. Cognitive Systems Research, 1(2), 99-118.
MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996). Psychological Review, 109(1), 35-54.
McDonald, R., & Satta, G. (2007). On the complexity of non-projective data-driven dependency parsing. In H. Bunt & P. Merlo (Eds.), Proceedings of the 10th international conference on parsing technologies (pp. 121-132). Prague: ACL.
Mel'cuk, I. (1988). Dependency syntax: Theory and practice. Albany, NY: State University of New York Press.
Miller, G. A. (1956). The magical number seven plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2), 81-97.
Miller, G. A., & Chomsky, N. (1963). Finitary models of language users. In R. B. R. D. Luce & E. Galanter (Eds.), Handbook of mathematical psychology (Vol. 2, pp. 419-492). New York: Wiley.
Miyake, A., & Shah, P. (1999). Models of working memory: Mechanisms of active maintenance and executive control. New York: Cambridge University Press.
Miyamoto, E., & Nakamura, M. (2003). Subject/object asymmetries in the processing of relative clauses in Japanese. In G. Garding & M. Tsujimura (Eds.), Proceedings of the West Coast conference on formal linguistics (Vol. 22, pp. 342-355). Somerville, MA: Casadilla Press.
Nakatani, K., & Gibson, E. (2010). An on-line study of Japanese nesting complexity. Cognitive Science, 34(1), 94-112.
Nederhof, M.-J. (1999). The computational complexity of the correct-prefix property for tags. Computational Linguistics, 25(3), 345-360.
Neuhaus, P., & Bröker, N. (1997). The complexity of recognition of linguistically adequate dependency grammars. In Proceedings of the 35th annual meeting of the Association for Computational Linguistics and eighth conference of the European chapter of the Association for Computational Linguistics (pp. 337-343). Stroudsburg, PA: Association for Computational Linguistics.
Nivre, J. (2005). Dependency grammar and dependency parsing. Technical report, Växjö University.
Nivre, J. (2009). Non-projective dependency parsing in expected linear time. In K. Su et al. (Eds.), Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: Volume 1. Suntec, Singapore: Association for Computational Linguistics.
Nivre, J., de Marneffe, M.-C., Ginter, F., Goldberg, Y., Hajic, J., Manning, C. D., McDonald, R., Petrov, S., Pyysalo, S., Silveira, N., Tsarfaty, R., & Zeman, D. (2016). Universal dependencies v1: A multilingual treebank collection. In N. Calzolari et al. (Eds.), Proceedings (pp.1659-1666). Portorož, Slovenia: European Language Resources Association (ELRA).
Pickering, M., & Barry, G. (1991). Sentence processing without empty categories. Language and Cognitive Processes, 6(3), 229-259.
Pitler, E., Kannan, S., & Marcus, M. (2013). Finding optimal 1-endpoint-crossing trees. Transactions of the Association for Computational Linguistics, 1, 13-24.
Rajakrishnan, R., van Schijndel, M., White, M., & Schuler, W. (2016). Investigating locality effects and surprisal in written English syntactic choice phenomena. Cognition, 155, 204-232.
Safavi, M. S., Husain, S., & Vasishth, S. (2016). Dependency resolution difficulty increases with distance in Persian separable complex predicates: Evidence for expectation and memory-based accounts. Frontiers in Psychology, 7, 403.
Shieber, S. M. (1985). Using restriction to extend parsing algorithms for complex-feature-based formalisms. In Proceedings of the 23rd annual meeting on Association for Computational Linguistics (pp. 145-152). Chicago: ACL.
Siewierska, A. (1998a). An overview of word order in Slavic languages. In A. Siewierska (Ed.), Constituent order in the languages of Europe (pp. 105-149). Berlin: Mouton de Gruyter.
Siewierska, A. (1998b). Variation in major constituent order: A global and a European perspective. In A. Siewierska (Ed.), Constituent order in the languages of Europe (pp. 475-551). Berlin: Mouton de Gruyter.
Staub, A. (2010). Eye movements and processing difficulty in object relative clauses. Cognition, 116(1), 71-86.
Straka, M., Hajic, J., Straková, J., & Hajic Jr., J. (2015). Parsing universal dependency treebanks using neural networks and search-based oracle. In M. Dickinson et al. (Eds.), International workship on treebanks and linguistic theories (pp.208-220). Warsaw: Institute of Computer Science.
Szmrecsányi, S. M. (2004). On operationalizing syntactic complexity. In C. F. G. Purnelle & A. Dister (Eds.), Le poids des mots. 7th international conference on textual data statistical analysis (pp. 1032-1039). Louvain-la-Neuve: Presses universitaires de Louvain.
Temperley, D. (2007). Minimization of dependency length in written English. Cognition, 105(2), 300-333.
Traxler, M. J., & Pickering, M. J. (1996). Plausibility and the processing of unbounded dependencies: An eye-tracking study. Journal of Memory and Language, 35(3), 454-475.
Vasishth, S., & Drenhaus, H. (2011). Locality in German. Dialogue and Discourse, 1, 59-82.
Vasishth, S., & Lewis, R. L. (2006). Argument-head distance and processing complexity: Explaining both locality and antilocality effects. Language, 82(4), 767-794.
Vasishth, S., Suckow, K., Lewis, R. L., & Kern, S. (2010). Short-term forgetting in sentence comprehension: Crosslinguistic evidence from verb-final structures. Language and Cognitive Processes, 25(4), 533-567.
Vijay-Shankar, K., & Joshi, A. K. (1985). Some computational properties of tree adjoining grammars. In Proceedings of the 23rd annual meeting on Association for Computational Linguistics (pp. 82-93). Chicago: ACL.
Wasow, T. (2002). Postverbal behavior (No. 145). Stanford University Center for the Study.
Wu, S., Bachrach, A., Cardenas, C., & Schuler, W. (2010). Complexity metrics in an incremental right-corner parser. In J. Hajič et al. (Eds.), Proceedings of the 48th annual meeting of the Association for Computational Linguistics (pp. 1189-1198). Uppsala, Sweden: Association for Computational Linguistics.
Yadav, H., Vaidya, A., & Husain, S. (2017). Understanding constraints on non-projectivity using novel measures. In S. Montemagni & J. Nivre (Eds.), Proceedings of the fourth international conference on dependency linguistics (depling 2017) (pp. 276-286). Pisa, Italy: Linköping University Electronic Press.
Yamashita, H., & Chang, F. (2001). “Long before short” preference in the production of a head-final language. Cognition, 81(2), B45-B55.
Yngve, V. H. (1960). A model and an hypothesis for language structure. Proceedings of the American Philosophical Society, 104(5), 444-466.