Luiz Amaral

On using intelligent computer-assisted language learning in real-life foreign language teaching and learning

This paper explores the motivation and prerequisites for successful integration of Intelligent Computer-Assisted Language Learning (ICALL) tools into current foreign language teaching and learning (FLTL) practice. We focus on two aspects, which we argue to be important for effective ICALL system development and use: (i) the relationship between activity design and restrictions needed to make natural language processing tractable and reliable, and (ii) pedagogical considerations and the influence of activity design choices on the integration of ICALL systems into FLTL practice.

Multiple Grammars and Second Language Representation

This paper presents an extension of the Multiple Grammars Theory (Roeper, 1999) to provide a formal mechanism that can serve as a generative-based alternative to current descriptive models of interlanguage. The theory extends historical work by Kroch and Taylor (1997), and has been taken into a computational direction by Yang (2003). The proposal is based on the idea that any human grammar readily accommodates sets of rules in sub-grammars that can seem (apparently) contradictory. We discuss the rationale behind this proposal and establish a dialogue with recent research in SLA, multilingualism, L3 acquisition, and L2 processing. We compare the Multiple Grammars explanation to optionality in L2 to other current proposals, and provide experimental results that can demonstrate the existence of active sub-grammars in the linguistic representation of L2 speakers.

Conceptualizing Student Models for ICALL

Student models for Intelligent Computer Assisted Language Learning (ICALL) have largely focused on the acquisition of grammatical structures. In this paper, we motivate a broader perspective of student models for ICALL that incorporates insights from current research on second language acquisition and language testing. We argue for a student model that includes a representation of the learners ability to use language to perform tasks as well as an explicit activity model that provides information on the language tasks and the inferences for the student model they support.

Tagging Portuguese with a Spanish tagger using cognates

We describe a knowledge and resource light system for an automatic morphological analysis and tagging of Brazilian Portuguese. We avoid the use of labor intensive resources; particularly, large annotated corpora and lexicons. Instead, we use (i) an annotated corpus of Peninsular Spanish, a language related to Portuguese, (ii) an unannotated corpus of Portuguese, (iii) a description of Portuguese morphology on the level of a basic grammar book. We extend the similar work that we have done (Hana et al., 2004; Feldman et al., 2006) by proposing an alternative algorithm for cognate transfer that effectively projects the Spanish emission probabilities into Portuguese. Our experiments use minimal new human effort and show 21% error reduction over even emissions on a fine-grained tagset.

From recording linguistic competence to supporting inferences about language acquisition in context

Student models for Intelligent Computer Assisted Language Learning (ICALL) have largely focused on the acquisition of grammatical structures. In this paper, we motivate a broader perspective of student models for ICALL that incorporates insights from current research on second language acquisition and language testing. We argue for a student model that includes a representation of the learners ability to use language in context and to perform tasks, as well as for an explicit activity model that provides information on the language tasks and the inferences for the student model they support. The student model architecture we present is being developed as part of the TAGARELA system, an intelligent workbook supporting the instruction of Portuguese.

Analyzing Learner Language: Towards a Flexible Natural Language Processing Architecture for Intelligent Language Tutors.

Intelligent language tutoring systems (ILTS) typically analyze learner input to diagnose learner language properties and provide individualized feedback. Despite a long history of ILTS research, such systems are virtually absent from real-life foreign language teaching (FLT). Taking a step toward more closely linking ILTS research to real-life FLT, in this article we investigate the connection between FLT activity design and the system architecture of an ILT system. We argue that a demand-driven, annotation-based natural language processing (NLP) architecture is well-suited to handle the demands posed by the heterogeneous learner input which results when supporting a wider range of FLT activity types. We illustrate how the unstructured information management architecture (UIMA) can be used in an ILTS, thereby connecting the specific needs of activities in foreign language teaching to the current research and development of NLP architectures in general. Making the conceptual issues concrete, we discuss the design and realization of a UIMA-based reimplementation of the NLP in the TAGARELA system, an intelligent web-based tutoring system supporting the teaching and learning of Portuguese.

Why minimal multiple rules provide a unique window into UG and L2

This article clarifies some ideas presented in this issue’s keynote article (Amaral and Roeper, this issue) and discusses several issues raised by the contributors’ comments on the nature of the Multiple Grammars (MG) theory. One of the key goals of the article is to unequivocally state that MG is not a parametric theory and that its current version follows very explicit minimalist assumptions. We also refine the notion of ‘minimal multiple rules’ to make their theoretical status more precise. Overall, we would like to acknowledge the important contribution of all the articles in this issue to the evolution of the theory.