Subject/Object Asymmetry in the Comprehension of English Relative Clauses by Japanese Learners of English

While a number of studies have shown that object-extracted relative clauses are more difficult to understand than subject-extracted counterparts for second language (L2) English learners (e.g., Izumi, 2003), less is known about why this is the case and how they process these complex sentences. This exploratory study examines the potential applicability of Gibson's (1998, 2000) Syntactic Prediction Locality Theory (SPLT), a theory proposed to predict first language (L1) processing difficulty, to L2 processing and considers whether the theory might also account for the processing difficulties of subject- and object-extracted relative clauses encountered by L2 learners. Results of a self-paced reading time experiment from 15 Japanese learners of English are mainly consistent with the reading time profile predicted by the SPLT and thus suggest that the L1 processing theory might also be able to account for L2 processing difficulty.

Syntactic and pragmatic constraints on processing relative clauses

Thc oen itninteureasc ttioo nb eo fa pcreangtmraal tiiscs uaen di ns ymnotadcetlisc ocfo nsestnrtaeinnctse processing. It is well established that object relatives (1) are harder to process than subject relatives (2). Passivization, other things being equal, increases sentence complexity. However, one of the functions of the passive construction is to promote an NP into the role of subject so that it can be more easily bound to the head NP in a higher clause. Thus, (3) is predicted to be marginally preferred over (1). Passiviazation in this instance may be seen as a way of avoiding the object relative construction. 1. The pipe that the traveller smoked annoyed the passengers. 2. The traveller that smoked the pipe annoyed the passengers. 3.The pipe that was smoked by the traveller annoyed the 4.The traveller that the pipe was smoked by annoyed the 5.The traveller that the lady was assaulted by annoyed the In (4) we have relativization of an NP which has been demoted by passivization to the status of a by-phrase. Such relative clauses may only be obtained under quite restrictive pragmatic conditions. Many languages do not permit relativization of a constituent as low as a by-phrase on the NP accessibility hierarchy (Comrie, 1984). The factors which determine the acceptability of demoted NP relatives like (4-5) reflect the ease with which the NP promoted to subject position can be taken as a discourse topic. We explored the acceptability of sentences such as (1-5) using pair-wise judgements of samddifferent meaning, accompanied by ratings of easeof understanding. Results are discussed with reference to Gibsons DLT model of linguistic complexity and sentence processing (Gibson, 2000)

Learning the dynamics of embedded clauses

Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations - generally in line with Siegelmann's theoretical work - which supply insights into how embedded structures of languages can be handled in analog hardware.