Publications

Abstract

The study develops a neurocomputational architecture for grammatical processing in language production and language comprehension (grammatical encoding and decoding, respectively). It seeks to answer two questions. First, how is online syntactic structure formation of the complexity required by natural-language grammars possible in a fixed, preexisting neural network without the need for online creation of new connections or associations? Second, is it realistic to assume that the seemingly disparate instantiations of syntactic structure formation in grammatical encoding and grammatical decoding can run on the same neural infrastructure? This issue is prompted by accumulating experimental evidence for the hypothesis that the mechanisms for grammatical decoding overlap with those for grammatical encoding to a considerable extent, thus inviting the hypothesis of a single “grammatical coder.” The paper answers both questions by providing the blueprint for a syntactic structure formation mechanism that is entirely based on prewired circuitry (except for referential processing, which relies on the rapid learning capacity of the hippocampal complex), and can subserve decoding as well as encoding tasks. The model builds on the “Unification Space” model of syntactic parsing developed by Vosse & Kempen (2000, 2008, 2009). The design includes a neurocomputational mechanism for the treatment of an important class of grammatical movement phenomena.

Abstract

Grammatical encoding and grammatical decoding (in sentence production and comprehension, respectively) are often portrayed as independent modalities of grammatical performance that only share declarative resources: lexicon and grammar. The processing resources subserving these modalities are supposed to be distinct. In particular, one assumes the existence of two workspaces where grammatical structures are assembled and temporarily maintained—one for each modality. An alternative theory holds that the two modalities share many of their processing resources and postulates a single mechanism for the online assemblage and short-term storage of grammatical structures: a shared workspace. We report two experiments with a novel “grammatical multitasking” paradigm: the participants had to read (i.e., decode) and to paraphrase (encode) sentences presented in fragments, responding to each input fragment as fast as possible with a fragment of the paraphrase. The main finding was that grammatical constraints with respect to upcoming input that emanate from decoded sentence fragments are immediately replaced by grammatical expectations emanating from the structure of the corresponding paraphrase fragments. This evidences that the two modalities have direct access to, and operate upon, the same (i.e., token-identical) grammatical structures. This is possible only if the grammatical encoding and decoding processes command the same, shared grammatical workspace. Theoretical implications for important forms of grammatical multitasking—self-monitoring, turn-taking in dialogue, speech shadowing, and simultaneous translation—are explored.

Abstract

Students who are learning to write in a foreign language, often want feedback on the grammatical quality of the sentences they produce. The usual NLP approach to this problem is based on parsing student-generated text. Here, we propose a generation-based ap- proach aiming at preventing errors ("scaffolding"). In our ICALL system, the student constructs sentences by composing syntactic trees out of lexically anchored "treelets" via a graphical drag & drop user interface. A natural-language generator computes all possible grammatically well-formed sentences entailed by the student-composed tree. It provides positive feedback if the student-composed tree belongs to the well-formed set, and negative feedback otherwise. If so requested by the student, it can substantiate the positive or negative feedback based on a comparison between the student-composed tree and its own trees (informative feedback on demand). In case of negative feedback, the system refuses to build the structure attempted by the student. Frequently occurring errors are handled in terms of "malrules." The system we describe is a prototype (implemented in JAVA and C++) which can be parameterized with respect to L1 and L2, the size of the lexicon, and the level of detail of the visually presented grammatical structures.