Publications

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.

Abstract

A new cognitive architecture is proposed for the syntactic aspects of human sentence processing. The architecture, called Unification Space, is biologically inspired but not based on neural nets. Instead it relies on biosynthesis as a basic metaphor. We use simulated annealing as an optimization technique which searches for the best configuration of isolated syntactic segments or subtrees in the final parse tree. The gradually decaying activation of individual syntactic nodes determines the ‘global excitation level’ of the system. This parameter serves the function of ‘computational temperature’ in simulated annealing. We have built a computer implementation of the architecture which simulates well-known sentence understanding phenomena. We report successful simulations of the psycholinguistic effects of clause embedding, minimal attachment, right association and lexical ambiguity. In addition, we simulated impaired sentence understanding as observable in agrammatic patients. Since the Unification Space allows for contextual (semantic and pragmatic) influences on the syntactic tree formation process, it belongs to the class of interactive sentence processing models.