Using Universal Dependencies in cross-linguistic complexity research

Berdičevskis, Aleksandrs; Çöltekin, Çağrı; Ehret, Katharina; Prince, Kilu von; Ross, Daniel; Thompson, Bill; Yan, Chunxiao; Demberg, Vera; Lupyan, Gary; Rama, Taraka; Bentz, Christian

doi:10.18653/v1/w18-6002

Cited by 19 publications

(17 citation statements)

References 14 publications

(14 reference statements)

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“…Third, TTR correlates quite well both with other, more advanced corpus-based measures and with manually compiled grammar-based measures of complexity (Bentz et al, 2016;Kettunen, 2014). Fourth, it performs no worse than average in a recent comparison of several corpus-based complexity measures (Berdicevskis et al, 2018).…”

Section: Measuring Complexitymentioning

confidence: 81%

Foreigner-directed speech is simpler than native-directed: Evidence from social media

Berdičevskis

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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I test two hypotheses that play an important role in modern sociolinguistics and language evolution studies: first, that non-native production is simpler than native; second, that production addressed to non-native speakers is simpler than that addressed to natives. The second hypothesis is particularly important for theories about contact-induced simplification, since the accommodation to non-natives may explain how the simplification can spread from adult learners to the whole community. To test the hypotheses, I create a very large corpus of native and non-native written speech in four languages (English, French, Italian, Spanish), extracting data from an internet forum where native languages of the participants are known and the structure of the interactions can be inferred. The corpus data yield inconsistent evidence with respect to the first hypothesis, but largely support the second one, suggesting that foreigner-directed speech is indeed simpler than native-directed. Importantly, when testing the first hypothesis, I contrast production of different speakers, which can introduce confounds and is a likely reason for the inconsistencies. When testing the second hypothesis, the comparison is always within the production of the same speaker (but with different addressees), which makes it more reliable.

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Section: Measuring Complexitymentioning

confidence: 81%

Foreigner-directed speech is simpler than native-directed: Evidence from social media

Berdičevskis

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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“…The factors evaluated in the above, from Nivre et al (2007); Van Asch and Daelemans (2010); Mc-Donald and Nivre (2011); Nivre and Fang (2017); Coltekin and Rama (2018); Berdicevskis et al (2018), were already discussed. A few other factors have been pointed at in the literature that were not applicable to our experiments: Søgaard and Haulrich (2010) show that the perplexity of the derivation orders of a transition-based dependency parser, is also predictive of parser performance.…”

Section: Related Workmentioning

confidence: 99%

“…POS bigram perplexity Others have proposed to use the perplexity of a POS bigram language model trained on the treebank's training section and applied to its test section, to predict parser performance (Coltekin and Rama, 2018;Berdicevskis et al, 2018).…”

Section: Factorsmentioning

confidence: 99%

Some Languages Seem Easier to Parse Because Their Treebanks Leak

Søgaard

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Cross-language differences in dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. In this paper I point to a factor not previously discussed: If we abstract away from words and dependency labels, how many graphs in the test data were seen in the training data? I discuss how to compute graph isomorphisms, and show that, treebank size aside, overlap between training and test graphs explains more of the observed variation than standard explanations such as the above.

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“…DLM is a constraint on the size of the whole flux of a sentence and therefore a particular case of constraints on the complexity of the flux. DLM is neither the only metrics for syntactic complexity (see Lewis (1996) for several constituency-based metrics; Berdicevskis et al 2018), nor the only metrics on the complexity of the flux and perhaps not the best. We will present other potentially interesting fluxbased metrics.…”

Section: Dlm-related Constraintsmentioning

confidence: 99%

Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)

2019

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The use of corpora for NLP and linguistics has only increased in recent years. In NLP, machine learning systems are by nature data-intensive, and in linguistics there is a renewed interest in the empirical validation of linguistic theory, particularly through corpus evidence. While the first statistical parsers have long been trained on the Penn treebank phrase structures, dependency treebanks, whether natively annotated with dependencies, or converted from phrase structures, have become more and more popular, as evidenced by the success of the Universal Dependency project, currently uniting 120 treebanks in 80 languages, annotated in the same dependency-based scheme. The availability of these resources has boosted empirical quantitative studies in syntax. It has also lead to a growing interest in theoretical questions around syntactic dependency, its history, its foundations, and the analyses of various constructions in dependency-based frameworks. Furthermore, the availability of large, multilingual annotated data sets, such as those provided by the Universal Dependencies project, has made cross-linguistic analysis possible to an extent that could only be dreamt of only a few years ago. In this context it was natural to bring together TLT (Treebanks and Linguistic Theories), the historical conference on treebanks as linguistic resources, Depling (The international conference on Dependency Linguistics), the conference uniting research on models and theories around dependency representations, and UDW (Universal Dependency Workshop), the annual meeting of the UD project itself. Moreover, in order to create a point of contact with the large community working in quantitative linguistics it seemed expedient to create a workshop dedicated to quantitative syntactic measures on treebanks and raw corpora, which gave rise to Quasy, the first workshop on Quantitative Syntax. And this led us to the first SyntaxFest. Because the potential audience and submissions to the four events were likely to have substantial overlap, we decided to have a single reviewing process for the whole SyntaxFest. Authors could choose to submit their paper to one or several of the four events, and in case of acceptance, the program co-chairs would decide which event to assign the accepted paper to.

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Using Universal Dependencies in cross-linguistic complexity research

Cited by 19 publications

References 14 publications

Foreigner-directed speech is simpler than native-directed: Evidence from social media

Foreigner-directed speech is simpler than native-directed: Evidence from social media

Some Languages Seem Easier to Parse Because Their Treebanks Leak

Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)

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