Using NLP for Enhancing Second Language Acquisition

Abstract

This study presents SMILLE, a system that draws on the Noticing Hypothesis and on input enhancements, addressing the lack of salience of grammatical information in online documents chosen by a given user. By means of input enhancements, the system can draw the user's attention to grammar, which could possibly lead to a higher intake per input ratio for metalinguistic information. The system receives as input an online document and submits it to a combined processing of parser and hand-written rules for detecting its grammatical structures. The input text can be freely chosen by the user, providing a more engaging experience and reflecting the user's interests. The system can enhance a total of 107 fine-grained types of grammatical structures that are based on the CEFR. An evaluation of some of those structures resulted in an overall precision of 87%.

When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection

“…Computer-assisted language learning CALL systems have been increasingly using NLP for creating learning content. Both SMILLE (Zilio et al, 2017) and WERTi (Meurers et al, 2010) aim to help the text understanding process by highlighting linguistic structures using hand-written rules and automatically acquired syntactic analysis. Apertium (Tyers et al, 2012), a rule-based MT system, while not aimed at language learning, does use human-and machine-readable rules, whose formalism can account for only fixed-length ordered contexts restricting their application.…”

Show, Don’t Tell: Visualising Finnish Word Formation in a Browser-Based Reading Assistant

“…Described first is the construction of the baseline reading assistant system. The system is by and large similar to existing systems such as GLOSSER (Nerbonne et al, 1998) or the reading assistant features of SMILLE (Zilio et al, 2017) or Revita (Katinskaia et al, 2017) -or even very widely used systems such as the WordNet-based alternative translations shown when a single word is selected in Google Translate. Notable as an improvement over some of those systems is Ni-inMikäOli?!…”

Proceedings of the 9th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2020)

“…Described first is the construction of the baseline reading assistant system. The system is by and large similar to existing systems such as GLOSSER (Nerbonne et al, 1998) or the reading assistant features of SMILLE (Zilio et al, 2017) or Revita -or even very widely used systems such as the WordNet-based alternative translations shown when a single word is selected in Google Translate. Notable as an improvement over some of those systems is Ni-inMikäOli?!…”