Unification-Based Parsing Applications for Intelligent Foreign Language Tutoring Systems

Hagen, L. Kirk

doi:10.1558/cj.v12i2-3.5-31

Cited by 11 publications

(3 citation statements)

References 11 publications

(3 reference statements)

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“…Using Mal-Rules in HPSG grammars has a long history. There have been efforts for English (Bender et al, 2004;Flickinger & Yu, 2013), Norwegian (Hellan et al, 2013), German (Heift, 1998), Spanish (Costa et al, 2006) and French (Hagen, 1994). From these, only English and Norwegian are still in active development.…”

Section: Mal-rules In Hpsgmentioning

confidence: 99%

Grammatical error detection using HPSG grammars: Diagnosing common Mandarin Chinese grammatical errors

Costa

Bond²

2022

hpsg

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Computational Grammars can be adapted to detect ungrammatical sentences, effectively transforming them into error detection (or correction) systems. In this paper we provide a theoretical account of how to adapt implemented HPSG grammars for grammatical error detection. We discuss how a single ungrammatical input can be reconstructed in multiple ways and, in turn, be used to provide specific, high-quality feedback to language learners. We then move on to exemplify this with a few of the most common error classes made by learners of Mandarin Chinese. We conclude with some notes concerning the adaptation and implementation of the methods described here in ZHONG, an open-source HPSG grammar for Mandarin Chinese.

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Section: Mal-rules In Hpsgmentioning

confidence: 99%

Grammatical error detection using HPSG grammars: Diagnosing common Mandarin Chinese grammatical errors

Costa

Bond²

2022

hpsg

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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).…”

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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“…Previous works exploring mal-rules in HPSG include English (Bender et al, 2004;Flickinger, 2010;Flickinger and Yu, 2013), Norwegian (Hellan et al, 2013), German (Heift, 1998), Spanish (Costa et al, 2006) and French (Hagen, 1994). From these, only English and Norwegian are known to still be in active development.…”

Section: Robust Parsing and Mal-rules In Hpsgmentioning

confidence: 99%

Using rich models of language in grammatical error detection

Costa¹

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Each of you have become an essential part of my life. Near or far, your love and support made even the most difficult days possible.I also want to thank my fellow members of NTU's Computational Linguistics Lab, for their friendship, for serving as inspiration, for sharing their knowledge, and for the many karaoke parties that made our lab feel like a great big family. To Michael, Sanghoun, Zhenzhen, Giulia, David, Mindy, Kuribayashi, Hanah, Siew Yeng. And a special mention to my dear friend Tuấn Anh, my favorite intellectual sparring partner, and his wife, Van, for her lovely friendship and for putting up with our long discussions over so many cups of coffee.I would also like to give a warm thanks to Kodrah Kristang and the Eurasian community in Singapore. It has been a pleasure to dedicate much of my little free time to the revitalization of Kristang in Singapore. Through this beautiful language I've made many friends -Fran, Kevin, Fuad, Andre, Gerald, Cass, Brenda and many others (some of whom have since parted). They have opened their homes and hearts to me, and made Singapore feel like a true home.And my thanks would not be complete without a special mention to my fellow game night companions -Francis, Quen, Hannah, Arthur, Siew Yeng, Mike, Ning, Rachel. Thank you for the friendship, the competition, and for the most fun way of escaping my dissertation when things weren't flowing. You've helped recharge my soul every time it needed it.Finally, this PhD thesis would also not been possible without the help of multiple generous sources of funding. In particular the work presented here received support from: i) my NTU/Singapore MOE Research Scholarship (RSS); ii) the Singapore MOE Tertiary Research Fund entitled Syntactic Well-Formedness Diagnosis and Error-Based Coaching in Computer Assisted Language Learning using Machine Translation Technology; iii) Fuji Xerox Corporation through a joint research project on Multilingual Semantic Analysis; iv) and the 2 EdeX Teaching and Learning Grant administered through NTU's Teaching, Learning and Pedagogy Division.

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Unification-Based Parsing Applications for Intelligent Foreign Language Tutoring Systems

Cited by 11 publications

References 11 publications

Grammatical error detection using HPSG grammars: Diagnosing common Mandarin Chinese grammatical errors

Grammatical error detection using HPSG grammars: Diagnosing common Mandarin Chinese grammatical errors

Bug Diagnosis By String Matching

Using rich models of language in grammatical error detection

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