# Graph Algorithms for Multiparallel Word Alignment

arXiv: https://arxiv.org/abs/2109.06283 · HTML: https://mlsanity.com/paper/2109.06283/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 65 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [Jointly Learning to Align and Translate with Transformer Models](https://mlsanity.com/paper/1909.02074/) (2019)
- [End-to-End Neural Word Alignment Outperforms GIZA++](https://mlsanity.com/paper/2004.14675/) (2020)
- [Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment](https://mlsanity.com/paper/2101.00148/) (2021)
- [Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages](https://mlsanity.com/paper/1704.08914/) (2017)
- [HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignment](https://mlsanity.com/paper/2003.07456/) (2020)
- [ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus](https://mlsanity.com/paper/2107.06632/) (2021)
- [Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages](https://mlsanity.com/paper/1401.6876/) (2014)

## Built on by (most cited first, 4 of 4)

- [Multilingual Sentence Transformer as A Multilingual Word Aligner](https://mlsanity.com/paper/2301.12140/) (2023)
- [Graph Neural Networks for Multiparallel Word Alignment](https://mlsanity.com/paper/2203.08654/) (2022)
- [BinaryAlign: Word Alignment as Binary Sequence Labeling](https://mlsanity.com/paper/2407.12881/) (2024)
- [UPRPRC: Unified Pipeline for Reproducing Parallel Resources -- Corpus from the United Nations](https://mlsanity.com/paper/2509.15789/) (2025)

## Abstract (arXiv)

With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently. Alignments are useful for typological research, transferring formatting like markup to translated texts, and can be used in the decoding of machine translation systems. At the same time, massively multilingual processing is becoming an important NLP scenario, and pretrained language and machine translation models that are truly multilingual are proposed. However, most alignment algorithms rely on bitexts only and do not leverage the fact that many parallel corpora are multiparallel. In this work, we exploit the multiparallelity of corpora by representing an initial set of bilingual alignments as a graph and then predicting additional edges in the graph. We present two graph algorithms for edge prediction: one inspired by recommender systems and one based on network link prediction. Our experimental results show absolute improvements in F1 of up to 28% over the baseline bilingual word aligner in different datasets.