Discontinuous Constituency and BERT: A Case Study of Dutch

Konstantinos Kogkalidis and Gijs Wijnholds

Assessing the ability of large-scale language models to automatically acquire aspects of linguistic theory has become a prominent theme in the literature ever since the inception of BERT [3] and its many variants, largely due to their unanticipated performance. Standard practice involves attaching BERT to a shallow neural model of low parametric complexity, and training the latter at detecting various linguistic patterns of interest, revealing in the process the amount to which they are encoded within BERT’s representations. The consensus points to BERT-like models having some capacity for syntactic understanding [7]. Their contextualized representations encode structural hierarchies [6] that can be projected into parse structures, using linear [4] or hyperbolic transformations [1], from which one can even obtain an accurate reconstruction of the underlying
constituency tree [8].

Despite their broadening scope, a latent bias persists in the insights provided by the probing literature, due to its focus being, by default, on English. English, albeit boasting a rich collection of evaluation resources, is characterized by a simple grammar with relatively few complications over the syntactic and morphological axes. Specifically when it comes to syntax, English lies in close
proximity to a context-free language, a class characterized by its low rank in terms of formal complexity and expressive power [2]. Perhaps more importantly, several commonly used evaluation test beds, including the Penn Treebank [5], are in themselves context-free, muddying the territory between probing for acquired syntactic generalization and arbitrating pattern extraction. As such,
claims about the syntactic skills of language models should not be assumed to freely transfer between languages (and, in some cases, even datasets).

In this paper, we seek to evaluate BERT in the face of patterns that go beyond context-freeness. We employ a mildly context-sensitive grammar formalism to generate complex patterns that do not naturally occur in English. We choose instead to experiment on Dutch, a language long-argued to be noncontext free, due it its capacity for exhibiting an arbitrary number of cross-serial dependencies. In Dutch, cross-serial dependencies arise in sentences where verbs form clusters, causing their respective dependencies with their arguments to intersect when drawn on a plane.

To that end, we first identify two well-studied constructions in Dutch that commonly involve cross-serial dependencies: control verb nesting and verb raising. We produce an artificial but naturalistic dataset of annotated samples for each construction; each sample contains span annotations for the verb- and nounphrases occurring within, as well as a mapping that associates each verb to its
corresponding subject. We then implement a probing model intended to select a verb’s subject from a number of candidate phrases, train it on a gold-standard resource of Dutch, and employ it on our data. Our experimental results convey a rapidly declining performance in the presence of discontinuous syntax, suggesting that the Dutch models investigated do not automatically learn to resolve the complex dependencies occurring in the language. To facilitate further research on the topic, our code is publicly available online.

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