An intelligent language tutoring system for handling errors caused by transfer

Wang, Yang; Garigliano, Roberto

doi:10.1007/3-540-55606-0_47

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…Over the past decade, a number of NLP systems have been implemented (Labrie & Singh, 1991;Levin & Evans, 1995;Loritz, 1995;Hagen, 1994;Holland et al, 1995;Sanders, 1991;Schwind, 1995;Wang & Garigliano, 1992;Yang & Akahori, 1997Heift & Nicholson, 2000) which, to some extent, also triggered a shift in the focus of research into the effectiveness of CALL. Whereas early research in CALL emphasised comparisons of CALL vs. traditional face-to-face instruction, over the past decade studies have focused more on comparisons of different CALL programs (Conrad, 1996).…”

Section: Introductionmentioning

confidence: 99%

Error-specific and individualised feedback in a Web-based language tutoring system: Do they read it?

Heift

2001

ReCALL

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This paper discusses learners’ responses to metalinguistic feedback and their strategies in error correction in a Web-based Intelligent Language Tutoring System (ILTS) for German. The system consists of a grammar and a parser which analyses student input and provides error-specific feedback to grammar and vocabulary exercises. Furthermore, the ILTS employs a Student Model that matches feedback messages to learner expertise and provides remedial exercises. Two beginner German classes, 33 students in all, used the ILTS for grammar practice, submitting a total of 4405 sentences for analysis. The results indicate that for the vast majority of sentences (79.5%) students read and attended to system feedback. Moreover, as iterations increased students paid more attention to the feedback messages.

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Section: Introductionmentioning

confidence: 99%

Error-specific and individualised feedback in a Web-based language tutoring system: Do they read it?

Heift

2001

ReCALL

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“…Many ILTSs are implemented in declarative representation formalism, including-notably-government binding (Chomsky, 1981;Chomsky, 1986), logical grammars (Colmerauer, 1978;Huang & Tokuda, 1996), definite clause grammar (Pereira & Warren, 1980), and head-driven phrase structure grammar (Hagen, 1994), among others. Several ILTSs resort to augmented transition networks (Woods, 1970;Wang & Garigliano, 1992;Catt & Hirst, 1990). To parse ill formed sentences, we use metarules by relaxing the constraints of strict grammars or buggy rules, which must be carefully designed to process all of the potentially ill formed constructions.…”

Section: Bug Diagnosis By String Matching: Application To Iltsmentioning

confidence: 99%

Bug Diagnosis By String Matching

Chen¹,

Tokuda²

2013

CALICO

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We have developed an entirely new template-automaton-based knowledge database system for an interactive intelligent language tutoring system (ILTS) for Japanese-English translation whereby model translations as well as a taxonomy of bugs extracted from ill formed translations typical of nonnative learners are collected. Unlike conventional rule-based systems whose complicated solution search procedure and labor-intensive processing have led to so-called knowledge engineer bottlenecks of the expert systems, the new dynamic programming-based heaviest common sequence (HCS) matching algorithm is both efficient and robust in which error diagnosis is implemented by selecting, from among many candidates' paths in the system template, a path having an HCS of a highest similarity with a student's free-format translation input. This best matched path to the given ill formed sentence is used to provide contingent feedback messages. We have laid down a theoretical framework for the global HCS matching algorithm which is applied to the dual form of acyclic weighted digraphs by topologically sorting the template automaton structured according to augmented transition networks. An extensive evaluation test of the diagnostic engine has ensured the validity, efficiency, and robustness of the algorithm in providing error-contingent feedback to a wide spectrum of learners even with different educational backgrounds.

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“…This correction procedure is language-specific (to English) and does not address the word order correction problem. Furthermore, the Chinese counterparts of the considered insertion candidates of articles, prepositions, and auxiliaries do not comprise the major errors occurring in sentences of English-Chinese language transfer, due to the characteristics of the Chinese language [10].…”

Section: Introductionmentioning

confidence: 99%

Word Order Correction for Language Transfer Using Relative Position Language Modeling

Liu

Harris

2008

2008 6th International Symposium on Chinese Spoken Language Processing

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Sentence correction has been an important and emerging issue in computer-assisted language learning. However, existing techniques based on grammar rules or statistical machine translation are still not robust enough to tackle the common incorrect word order errors in sentences produced by second language learners of Chinese. In this paper, a novel relative position language model is proposed to address this problem, for which a corpus of erroneous English-Chinese language transfer sentences along with their corrected counterparts is created and manually judged by human annotators. Experimental results show that compared to a scoring approach based on an n-gram language model and a phrase-based machine translation system, the performance in terms of BLEU scores of the proposed approach achieved improvements of 20.3% and 26.5% for the correction of word order errors resulting from language transfer, respectively.

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An intelligent language tutoring system for handling errors caused by transfer

Cited by 10 publications

References 6 publications

Error-specific and individualised feedback in a Web-based language tutoring system: Do they read it?

Error-specific and individualised feedback in a Web-based language tutoring system: Do they read it?

Bug Diagnosis By String Matching

Word Order Correction for Language Transfer Using Relative Position Language Modeling

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