mlsanityData to 7 Oct 2026

Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing

arXiv 2201.11374 ↗First posted 27 Jan 2022cs.CLcited by 3

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Abstract (arXiv)

In this work, we focus on low-resource dependency parsing for multiple languages. Several strategies are tailored to enhance performance in low-resource scenarios. While these are well-known to the community, it is not trivial to select the best-performing combination of these strategies for a low-resource language that we are interested in, and not much attention has been given to measuring the efficacy of these strategies. We experiment with 5 low-resource strategies for our ensembled approach on 7 Universal Dependency (UD) low-resource languages. Our exhaustive experimentation on these languages supports the effective improvements for languages not covered in pretrained models. We show a successful application of the ensembled system on a truly low-resource language Sanskrit. The code and data are available at: https://github.com/Jivnesh/SanDP

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 53 references that are in the map. First posted on arXiv 27 Jan 2022.

Builds on

27 of 53 references.

References · 27

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15 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2025Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?cited by 2
  2. 2023Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskritcited by 1
  3. 2025A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMscited by 1