Papers with word alignment methods
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (2020.findings-emnlp)
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| Challenge: | Word alignments are useful for statistical and neural machine translation (NMT) and cross-lingual annotation projection. |
| Approach: | They propose to leverage multilingual word embeddings for word alignment. |
| Outcome: | The proposed methods perform better for four languages and comparable for two languages than traditional statistical aligners even with abundant parallel data. |
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models (2023.eacl-main)
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Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
| Challenge: | Large multilingual models have inspired a new class of word alignment methods, which work well for pretraining languages. |
| Approach: | They propose to use transformer-based word alignment methods to extract alignments from massive pretrained models. |
| Outcome: | The proposed methods outperform traditional methods for languages unseen to pretraining models, and are competitive with each other. |
WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction (2023.acl-long)
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| Challenge: | Existing word alignment methods rely on manual data and lack generalization ability. |
| Approach: | They propose to use a weakly-supervised large-scale weakly supervised dataset for word alignment pre-training via span prediction to reduce the need for manual data. |
| Outcome: | The proposed method improves upon the best supervised baseline by 3.3 6.1 points in F1 and 1.5 6.1 point in AER. |