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

arXiv: https://arxiv.org/abs/2206.05154 · HTML: https://mlsanity.com/paper/2206.05154/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 75 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## 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)

## Built on by (most cited first, 3 of 3)

- [GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text](https://mlsanity.com/paper/2403.06399/) (2024)
- [Massively Multilingual Joint Segmentation and Glossing](https://mlsanity.com/paper/2601.10925/) (2026)
- [Speaking of Language: Reflections on Metalanguage Research in NLP](https://mlsanity.com/paper/2604.02645/) (2026)

## Abstract (arXiv)

One of the challenges of language teaching is how to organize the rules regarding syntax, semantics, or phonology of the language in a meaningful manner. This not only requires pedagogical skills, but also requires a deep understanding of that language. 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 process is challenging because i) it requires that such experts be accessible and have the necessary resources, and ii) even if there are such experts, describing all the intricacies of a language is time-consuming and prone to omission. In this article, we present an automatic framework that aims to facilitate this process by automatically discovering and visualizing descriptions of different aspects of grammar. Specifically, 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) and show illustrative examples. We apply this method for teaching the Indian languages, Kannada and Marathi, which, unlike English, do not have well-developed pedagogical resources and, therefore, are likely to benefit from this exercise. To assess the perceived utility of the extracted material, we enlist the help of language educators from schools in North America who teach these languages to perform a manual evaluation. Overall, teachers find the materials to be interesting as a reference material for their own lesson preparation or even for learner evaluation.