Exploiting Learners’ Tendencies for Detecting English Determiner Errors

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Vilenius²,

Whittaker

2014

Self Cite

This paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and correcting preposition errors. Their most distinct advantage is that they can correct errors with feedback messages explaining why the preposition is erroneous. This paper proposes a method for automatically generating them by comparing learner and native corpora. Experiments show (i) automatically generated error case frames achieve a performance comparable to conventional methods; (ii) error case frames are intuitively interpretable and manually modifiable to improve them; (iii) feedback messages provided by error case frames are effective in language learning assistance. Considering these advantages and the fact that it has been difficult to provide feedback messages by automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.

Section: Errormentioning

confidence: 99%

Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Vilenius²,

Whittaker

2014

Self Cite

“…Unfortunately, however, the discrepancy between a POS tagger and its target text often results in POS-tagging errors, which in turn leads to performance degradation in related tasks as Nagata and Kawai (2011) and Bryant et al (2017) show. Specifically, a wide variety of characteristic phenomena that potentially degrade POS tagging performance appear in learner English.…”

Section: Introductionmentioning

confidence: 99%

A POS Tagging Model Adapted to Learner English

Proceedings of the 2018 EMNLP Workshop W-Nut: The 4th Workshop on Noisy User-Generated Text

Mizumoto

Kikuchi³

et al. 2018

Self Cite

There has been very limited work on the adaptation of Part-Of-Speech (POS) tagging to learner English despite the fact that POS tagging is widely used in related tasks. In this paper, we explore how we can adapt POS tagging to learner English efficiently and effectively. Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this. Considering the previous findings and the discussion, we introduce the design of our model based on bidirectional Long Short-Term Memory. In addition, we describe how to adapt it to a wide variety of native languages (potentially, hundreds of them). In the evaluation section, we empirically show that it is effective for POS tagging in learner English, achieving an accuracy of 0.964, which significantly outperforms the state-of-the-art POS-tagger. We further investigate the tagging results in detail, revealing which part of the model design does or does not improve the performance.

“…Current methods mostly exploit machine learning-based classifiers to correct target errors; examples are errors in article [2]- [4], preposition [4]- [7], and tense [8], [9], to name a few. More recently, Wu and Ng [10] and Rozovskaya and Roth [11] proposed methods for simultaneously correcting multiple types of errors.…”

Section: Introductionmentioning

confidence: 99%

A Method for Correcting Preposition Errors in Learner English with Feedback Messages

IEICE Trans. Inf. & Syst.

Whittaker²

2017

Self Cite

SUMMARYThis paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and correcting preposition errors. Their most distinct advantage is that they can correct errors with feedback messages explaining why the preposition is erroneous. This paper proposes a method for automatically generating them by comparing learner and native corpora. Experiments show (i) automatically generated error case frames achieve a performance comparable to previous methods; (ii) error case frames are intuitively interpretable and manually modifiable to improve them; (iii) feedback messages provided by error case frames are effective in language learning assistance. Considering these advantages and the fact that it has been difficult to provide feedback messages using automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.