mlsanityData to 7 Oct 2026

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

arXiv 2210.06207 ↗First posted 12 Oct 2022cs.CLcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 54 references that are in the map. First posted on arXiv 12 Oct 2022.

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References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  4. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
  5. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  6. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  7. 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
  8. 2020SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddingscited by 155
  9. 2019Multilingual is not enough: BERT for Finnishcited by 118
  10. 2019Jointly Learning to Align and Translate with Transformer Modelscited by 75
  11. 2022Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptationcited by 44
  12. 2019Adding Interpretable Attention to Neural Translation Models Improves Word Alignmentcited by 30
5 more
  1. 2020UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Datacited by 24
  2. 2018On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translationcited by 17
  3. 2022Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languagescited by 8
  4. 2020HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignmentcited by 4
  5. 2020Subword Sampling for Low Resource Word Alignmentcited by 4

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