mlsanityData to 7 Oct 2026

AUTOLEX: An Automatic Framework for Linguistic Exploration

arXiv 2203.13901 ↗First posted 25 Mar 2022cs.CLcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 48 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

10 of 48 references.

References · 10

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  4. 201975 Languages, 1 Model: Parsing Universal Dependencies Universallycited by 90
  5. 2016Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representationscited by 55
  6. 2017SPINE: SParse Interpretable Neural Embeddingscited by 55
  7. 2020Automatic Extraction of Rules Governing Morphological Agreementcited by 9
  8. 2018Neural Factor Graph Models for Cross-lingual Morphological Taggingcited by 8
  9. 2021Evaluating the Morphosyntactic Well-formedness of Generated Textscited by 8
  10. 2017Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learningcited by 4

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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.