Machine Translation into Learning - Luis Morgado da Costa - FOSSASIA Summit 2017

Published on: Saturday, 18 March 2017

Speaker(s): Luis Morgado da Costa (Singapore)

We present a novel approach to intelligent computer assisted language learning (iCALL) systems, using deep syntactic parsers and semantic based machine translation in the diagnosing and providing explicit feedback on users’ language errors, using Mandarin Chinese (L2) as a case study. In this presentation we describe the proposed approach and current progress of a recently funded two year project. During this project, we are developing a proof of concept system showing how semantic based machine translation can, in conjunction with robust computational grammars, be used to interact with students, better understand their language errors, and help students correct their grammar through a series of useful feedback messages and guided language drills.Ultimately, we aim to prove the viability of a new integrated rule-based MT approach to disambiguate students’ intended meaning in computer-assisted language teaching. This is a necessary step to provide accurate coaching on how to correct ungrammatical input, and it will allow us to overcome the current bottleneck in Computer Assisted Language Learning (CALL) – an exponential burst of ambiguity caused by ambiguous lexical items.

(Type: Talk | Track: AI & Machine Learning | Room: Mendel (Ground Floor))

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