mlsanityData to 7 Oct 2026

Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages

arXiv 2201.12219 ↗First posted 28 Jan 2022cs.CLcited by 8

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Abstract (arXiv)

Parallel corpora are ideal for extracting a multilingual named entity (MNE) resource, i.e., a dataset of names translated into multiple languages. Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages. We present CLC-BN, a new method for creating an MNE resource, and apply it to the Parallel Bible Corpus, a corpus of more than 1000 languages. CLC-BN learns a neural transliteration model from parallel-corpus statistics, without requiring any other bilingual resources, word aligners, or seed data. Experimental results show that CLC-BN clearly outperforms prior work. We release an MNE resource for 1340 languages and demonstrate its effectiveness in two downstream tasks: knowledge graph augmentation and bilingual lexicon induction.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 65 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

24 of 65 references.

References · 24

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12 more
  1. 2020SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddingscited by 155
  2. 2018A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddingscited by 145
  3. 2018Gromov-Wasserstein Alignment of Word Embedding Spacescited by 119
  4. 2019A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelscited by 117
  5. 2017Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projectioncited by 36
  6. 2014POLYGLOT-NER: Massive Multilingual Named Entity Recognitioncited by 32
  7. 2019Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?cited by 31
  8. 2019Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resourcescited by 25
  9. 2020UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Datacited by 24
  10. 2019Exact Hard Monotonic Attention for Character-Level Transductioncited by 22
  11. 2021Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignmentcited by 9
  12. 2021On Biasing Transformer Attention Towards Monotonicitycited by 5

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 2; 2025: 1.

8 papers.

  1. 2023Glot500: Scaling Multilingual Corpora and Language Models to 500 Languagescited by 116
  2. 2024Goldfish: Monolingual Language Models for 350 Languagescited by 27
  3. 2023A Crosslingual Investigation of Conceptualization in 1335 Languagescited by 11
  4. 2022Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Taggingcited by 4
  5. 2022Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddingscited by 1
  6. 2022SilverAlign: MT-Based Silver Data Algorithm For Evaluating Word Alignmentcited by 0
  7. 2024Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languagescited by 0
  8. 2025How Good is BLI as an Alignment Measure: A Study in Word Embedding Paradigmcited by 0