RESEARCH

NLP

On the Distribution of Deep Clausal Embeddings:A Large Cross-linguistic Study

July 26, 2019

Abstract

Embedding a clause inside another (“the girl[who likes cars [that run fast]] has arrived”) is a fundamental resource that has been argued to be a key driver of linguistic expressiveness. As such, it plays a central role in fundamental debates on what makes human language unique, and how they might have evolved.Empirical evidence on the prevalence and the limits of embeddings has however been based on either laboratory setups or corpus data of relatively limited size. We introduce here a collection of large, dependency-parsed written corpora in17languages, that allow us, for the first time, to capture clausal embedding through dependency graphs and assess their distribution. Our results indicate that there is no evidence for hard constraint son embedding depth: the tail of depth distributions is heavy. Moreover, although deeply embedded clauses tend to be shorter, suggesting processing load issues, complex sentences with many embeddings do not display a bias towards less deep embeddings. Taken together,the results suggest that deep embeddings are not disfavored in written language. More generally, our study illustrates how resources and methods from latest-generation big-data Nolan provide new perspectives on fundamental questions in theoretical linguistics.

Download the Paper

AUTHORS

Written by

Marco Baroni

Ryan Cotterell

Balthasar Bickel

Damian Blasi

Lawrence Wolf-Sonkin

Sabine Stoll

Publisher

ACL

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