# SilverAlign: MT-Based Silver Data Algorithm For Evaluating Word Alignment

arXiv: https://arxiv.org/abs/2210.06207 · HTML: https://mlsanity.com/paper/2210.06207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [Jointly Learning to Align and Translate with Transformer Models](https://mlsanity.com/paper/1909.02074/) (2019)
- [Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation](https://mlsanity.com/paper/2203.09435/) (2022)
- [Adding Interpretable Attention to Neural Translation Models Improves Word Alignment](https://mlsanity.com/paper/1901.11359/) (2019)
- [UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data](https://mlsanity.com/paper/2007.07683/) (2020)
- [On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation](https://mlsanity.com/paper/1809.03985/) (2018)
- [Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages](https://mlsanity.com/paper/2201.12219/) (2022)
- [HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignment](https://mlsanity.com/paper/2003.07456/) (2020)
- [Subword Sampling for Low Resource Word Alignment](https://mlsanity.com/paper/2012.11657/) (2020)

## Abstract (arXiv)

Word alignments are essential for a variety of NLP tasks. Therefore, choosing the best approaches for their creation is crucial. However, the scarce availability of gold evaluation data makes the choice difficult. We propose SilverAlign, a new method to automatically create silver data for the evaluation of word aligners by exploiting machine translation and minimal pairs. We show that performance on our silver data correlates well with gold benchmarks for 9 language pairs, making our approach a valid resource for evaluation of different domains and languages when gold data are not available. This addresses the important scenario of missing gold data alignments for low-resource languages.