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

arXiv: https://arxiv.org/abs/2201.12219 · HTML: https://mlsanity.com/paper/2201.12219/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings](https://mlsanity.com/paper/1805.06297/) (2018)
- [Gromov-Wasserstein Alignment of Word Embedding Spaces](https://mlsanity.com/paper/1809.00013/) (2018)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection](https://mlsanity.com/paper/1707.02483/) (2017)
- [POLYGLOT-NER: Massive Multilingual Named Entity Recognition](https://mlsanity.com/paper/1410.3791/) (2014)
- [Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?](https://mlsanity.com/paper/1909.01638/) (2019)
- [Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources](https://mlsanity.com/paper/1911.06161/) (2019)
- [UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data](https://mlsanity.com/paper/2007.07683/) (2020)
- [Exact Hard Monotonic Attention for Character-Level Transduction](https://mlsanity.com/paper/1905.06319/) (2019)
- [Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment](https://mlsanity.com/paper/2101.11112/) (2021)
- [On Biasing Transformer Attention Towards Monotonicity](https://mlsanity.com/paper/2104.03945/) (2021)

## Built on by (most cited first, 8 of 8)

- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [A Crosslingual Investigation of Conceptualization in 1335 Languages](https://mlsanity.com/paper/2305.08475/) (2023)
- [Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging](https://mlsanity.com/paper/2210.09840/) (2022)
- [Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings](https://mlsanity.com/paper/2205.15713/) (2022)
- [SilverAlign: MT-Based Silver Data Algorithm For Evaluating Word Alignment](https://mlsanity.com/paper/2210.06207/) (2022)
- [Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages](https://mlsanity.com/paper/2406.16030/) (2024)
- [How Good is BLI as an Alignment Measure: A Study in Word Embedding Paradigm](https://mlsanity.com/paper/2511.13040/) (2025)

## 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.