# AUTOLEX: An Automatic Framework for Linguistic Exploration

arXiv: https://arxiv.org/abs/2203.13901 · HTML: https://mlsanity.com/paper/2203.13901/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [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)
- [SPINE: SParse Interpretable Neural Embeddings](https://mlsanity.com/paper/1711.08792/) (2017)
- [Automatic Extraction of Rules Governing Morphological Agreement](https://mlsanity.com/paper/2010.01160/) (2020)
- [Neural Factor Graph Models for Cross-lingual Morphological Tagging](https://mlsanity.com/paper/1805.04570/) (2018)
- [Evaluating the Morphosyntactic Well-formedness of Generated Texts](https://mlsanity.com/paper/2103.16590/) (2021)
- [Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning](https://mlsanity.com/paper/1710.03877/) (2017)

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

- [Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning](https://mlsanity.com/paper/2206.05154/) (2022)
- [Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning](https://mlsanity.com/paper/2310.18417/) (2023)
- [TAMS: Translation-Assisted Morphological Segmentation](https://mlsanity.com/paper/2403.14840/) (2024)

## Abstract (arXiv)

Each language has its own complex systems of word, phrase, and sentence construction, the guiding principles of which are often summarized in grammar descriptions for the consumption of linguists or language learners. However, manual creation of such descriptions is a fraught process, as creating descriptions which describe the language in "its own terms" without bias or error requires both a deep understanding of the language at hand and linguistics as a whole. We propose an automatic framework AutoLEX that aims to ease linguists' discovery and extraction of concise descriptions of linguistic phenomena. Specifically, we apply this framework to extract descriptions for three phenomena: morphological agreement, case marking, and word order, across several languages. We evaluate the descriptions with the help of language experts and propose a method for automated evaluation when human evaluation is infeasible.