# Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning

arXiv: https://arxiv.org/abs/2310.18417 · HTML: https://mlsanity.com/paper/2310.18417/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 75 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited](https://mlsanity.com/paper/1908.07397/) (2019)
- [A Baseline Readability Model for Cebuano](https://mlsanity.com/paper/2203.17225/) (2022)
- [Automatic Extraction of Rules Governing Morphological Agreement](https://mlsanity.com/paper/2010.01160/) (2020)
- [When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection](https://mlsanity.com/paper/2109.06014/) (2021)
- [Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning](https://mlsanity.com/paper/1710.03877/) (2017)
- [AUTOLEX: An Automatic Framework for Linguistic Exploration](https://mlsanity.com/paper/2203.13901/) (2022)

## Abstract (arXiv)

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.