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Citation guide

This page is for researchers who want to cite, compare, or reproduce me_fasttext.

Cite the paper

If the method, implementation, benchmark protocol, or compact serving layout is useful in your work, please cite the arXiv paper:

@misc{du2026memoryefficientfasttext,
  title         = {Memory-Efficient FastText: A Comprehensive Approach Using Double-Array Trie Structures and Mark-Compact Memory Management},
  author        = {Yimin Du},
  year          = {2026},
  eprint        = {2506.01254},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2506.01254}
}

When to cite

me_fasttext is most relevant when your work discusses:

  • FastText-style word or subword embeddings
  • OOV-heavy retrieval, ranking, or classification
  • hash collisions in character n-gram embedding tables
  • trie-backed dictionaries or double-array trie lookup for NLP
  • memory-efficient embedding storage
  • mmap-based model serving
  • compact inference artifacts for large vocabularies
  • lexical retrieval layers used before dense reranking

Short related-work wording

Use or adapt one of these sentences when citing the paper.

For FastText and subword embeddings:

Du revisits FastText-style subword embeddings from a systems perspective, replacing hash-bucketed n-gram ids with exact trie-backed ids before compact serving export.

For memory-efficient serving:

me_fasttext separates n-gram identity from serving layout: it trains with exact subword ids, then rewrites retained rows into a compact mmap-ready artifact.

For retrieval pipelines:

The method is useful as a low-cost lexical embedding layer for OOV-heavy retrieval and candidate generation before dense reranking.

For data-structure readers:

The implementation uses trie-backed word and n-gram dictionaries so lexical identities remain inspectable before conservative row sharing.

Reproduction checklist

When comparing against this work, please report:

  • git commit and release tag
  • corpus language, token count, document count, and preprocessing
  • vocabulary size and extracted n-gram count
  • training mode and hyperparameters
  • memory, artifact size, cold load time, and warm query latency
  • downstream quality metric
  • hardware, storage, OS, compiler, and filesystem

The benchmark protocol includes a fuller report template.

Code citation metadata

This repository includes:

  • CITATION.cff for GitHub citation UI and reference managers
  • .zenodo.json for DOI-ready Zenodo metadata
  • release notes for the v0.1.0 research prototype

If a Zenodo DOI is enabled later, cite the DOI for the exact software snapshot and cite the arXiv paper for the method.