Teacher Perception of Automatically Extracted Grammar Concepts for L2
  Language Learning
Aditi Chaudhary Arun Sampath Ashwin Sheshadri Antonios Anastasopoulos Graham Neubig
One of the challenges in language teaching is how best to organize rules
regarding syntax, semantics, or phonology in a meaningful manner. This not only
requires content creators to have pedagogical skills, but also have that
language's deep understanding. While comprehensive materials to develop such
curricula are available in English and some broadly spoken languages, for many
other languages, teachers need to manually create them in response to their
students' needs. This is challenging because i) it requires that such experts
be accessible and have the necessary resources, and ii) describing all the
intricacies of a language is time-consuming and prone to omission. In this
work, we aim to facilitate this process by automatically discovering and
visualizing grammar descriptions. We extract descriptions from a natural text
corpus that answer questions about morphosyntax (learning of word order,
agreement, case marking, or word formation) and semantics (learning of
vocabulary). We apply this method for teaching two Indian languages, Kannada
and Marathi, which, unlike English, do not have well-developed resources for
second language learning. To assess the perceived utility of the extracted
material, we enlist the help of language educators from schools in North
America to perform a manual evaluation, who find the materials have potential
to be used for their lesson preparation and learner evaluation.