Publications

Abstract

In present-day spoken German, subordinate clauses introduced by the connector weil ‘because’ occur with two orders of subject, finite verb, and object(s). In addition to weil clauses with verb-final word order (“VF”; standard in subordinate clauses) one often hears weil clauses with SVO, the standard order of main clauses (“verb-second”, V2). The “weil-V2” phenomenon is restricted to sentences where the weil clause follows the main clause, and is virtually absent from formal (written, edited) German, occurring only in extemporaneous speech. Extant accounts of weil-V2 focus on the interpretation of weil-V2 clauses by the hearer, in particular on the type of discourse relation licensed by weil-V2 vs. weil-VF: causal/propositional or inferential/epistemic. Focusing instead on the production of weil clauses by the speaker, we examine a collection of about 1,000 sentences featuring a causal connector (weil, da or denn) after the main clause, all extracted from a corpus of spoken German dialogues and annotated with tags denoting major prosodic and syntactic boundaries, and various types of disfluencies (pauses, hesitations). Based on the observed frequency patterns and on known linguistic properties of the connectors, we propose that weil-V2 is caused by miscoordination between the mechanisms for lexical retrieval and grammatical encoding: Due to its high frequency, the lexical item weil is often selected prematurely, while the grammatical encoder is still working on the syntactic shape of the weil clause. Weil-V2 arises when pragmatic and processing factors drive the encoder to discontinue the current sentence, and to plan the clause following weil in the form of the main clause of an independent, new sentence. Thus, the speaker continues with a V2 clause, seemingly in violation of the VF constraint imposed by the preceding weil. We also explore implications of the model regarding the interpretation of sentences containing causal connectors.

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.