model

In this paper, we show that good structured codes over non-Abelian groups do exist. Specifically, we construct codes over the smallest non-Abelian group D6 and show that the performance of these codes is superior to the performance of Abelian group codes of the same alphabet size. This promises the possibility of using non-Abelian codes for multi-terminal settings where the structure of the cod...

The role of acquired error-detection capabilities in skill learning was investigated by manipulating the delay of knowledge of results (KR). Compared with delayed feedback, instantaneous KR should be detrimental to the learning of error-detection capabilities because it should tend to block spontaneous subjective evaluation of response-produced feedback. Weaker error-detection capabilities shou...

This paper describes a dialogue-based CALL (Computer Assisted Language Learning) system. One of the major problems in CALL systems is that learners are usually assigned a passive role. Learners have no practices in composing their own utterances. The other major problem is that lots of conventional CALL systems are pronunciation exercise systems. However, pronunciation exercise is only a part o...

We describe the NUS Corpus of Learner English (NUCLE), a large, fully annotated corpus of learner English that is freely available for research purposes. The goal of the corpus is to provide a large data resource for the development and evaluation of grammatical error correction systems. Although NUCLE has been available for almost two years, there has been no reference paper that describes the...

This paper presents a novel approach to error correction in content words in learner writing focussing on adjective–noun (AN) combinations. We show how error patterns can be used to improve the performance of the error correction system, and demonstrate that our approach is capable of suggesting an appropriate correction within the top two alternatives in half of the cases and within top 10 alt...