Automatic grammar correction for second-language learners

Lee, John; Seneff, Stephanie

doi:10.21437/interspeech.2006-542

Cited by 26 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatic error detection and correction of sentences produced by second language learners has been an emerging field in computer-assisted language learning (CALL) [1][2][3][4]. Although error correction and detection of erroneous sentences are two related research topics that can work complementarily, they are considered as two distinct problems in contemporary approaches.…”

Section: Introductionmentioning

confidence: 99%

“…In the ALEK system (Assessing LExical Knowledge) developed by [2], detection of a sentence with errors is addressed using 20 target words extracted from the Test of English as a Foreign Language (TOEFL). In [1], the errors for correction, according to the Japanese Learner's English corpus [3], focus on those involving specific partsof-speech such as articles and number agreement. In [5], the countability errors encountered in English as a Second Language (ESL) are considered for correction using the Chinese Learner English Corpus (CLEC).…”

Section: Introductionmentioning

confidence: 99%

“…There are several approaches to the problem of error correction, or grammar correction, for second language learners. Natural language generation (NLG) from meaning representations is one possible direction to deal with the sentence correction problem [1]. However, since it requires in-depth analysis on not only the semantics of sentences but also the semantics of ill-formed sentences, it is not currently feasible to yield the meaning representations for the purpose of error correction [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…An intermediate method between the above two NLG approaches is presented in [1], which we refer to as the Stripping and Insertion of Limited Candidates (SILC) approach. This correction procedure is language-specific (to English) and does not address the word order correction problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Word Order Correction for Language Transfer Using Relative Position Language Modeling

Liu,

Wu,

Harris

2008

Int. Symp. On Chinese Spoken Language Processing

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Word Order Correction for Language Transfer Using Relative Position Language Modeling

Liu,

Wu,

Harris

2008

Int. Symp. On Chinese Spoken Language Processing

View full text Add to dashboard Cite

show abstract

“…Any multimodal interactions need to be intuitive and easily integrated into the conversational thread. Finally, computers should be able to analyze the recorded utterances, and, in a subsequent interaction, critique selected production errors, involving aspects such as phonetic accuracy [5,20], tone production [21,15], lexical and grammar usage [16], and fluency [9].…”

Section: Introductionmentioning

confidence: 99%

Web-based dialogue and translation games for spoken language learning

Seneff

2007

Speech and Language Technology in Education (SLaTE 2007)

View full text Add to dashboard Cite

It is widely recognized that one of the best ways to learn a foreign language is through spoken dialogue with a native speaker. However, this is not a practical method in the classroom due to the one-to-one student/teacher ratio it implies. A potential solution to this problem is to rely on computer spoken dialogue systems to role play a conversational partner. This paper describes several multilingual dialogue systems specifically designed to address this need. Students can engage in dialogue with the computer either over the telephone or through audio/typed input at a Web page. Several different domains are being developed, in which a student's conversational interaction is assisted by a software agent functioning as a "tutor" which can provide them with translation assistance at any time. Some of the research issues surrounding high-quality spoken language translation and dialogue modeling for language games are discussed.

show abstract

Grammatical and context‐sensitive error correction using a statistical machine translation framework

Ehsan

Faili

2012

Softw Pract Exp

View full text Add to dashboard Cite

SUMMARY Producing electronic rather than paper documents has considerable benefits such as easier organizing and data management. Therefore, existence of automatic writing assistance tools such as spell and grammar checker/correctors can increase the quality of electronic texts by removing noise and correcting the erroneous sentences. Different kinds of errors in a text can be categorized into spelling, grammatical and real‐word errors. In this article, we present a language‐independent approach based on a statistical machine translation framework to develop a proofreading tool, which detects grammatical errors as well as context‐sensitive spelling mistakes (real‐word errors). A hybrid model for grammar checking is suggested by combining the mentioned approach with an existing rule‐based grammar checker. Experimental results on both English and Persian languages indicate that the proposed statistical method and the rule‐based grammar checker are complementary in detecting and correcting syntactic errors. The results of the hybrid grammar checker, applied to some English texts, show an improvement of about 24% with respect to the recall metric with almost similar value for precision. Experiments on real‐world data set show that state‐of‐the‐art results are achieved for grammar checking and context‐sensitive spell checking for Persian language. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Automatic grammar correction for second-language learners

Cited by 26 publications

References 14 publications

Word Order Correction for Language Transfer Using Relative Position Language Modeling

Word Order Correction for Language Transfer Using Relative Position Language Modeling

Web-based dialogue and translation games for spoken language learning

Grammatical and context‐sensitive error correction using a statistical machine translation framework

Contact Info

Product

Resources

About