Papers with word alignment tasks
A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment (2021.acl-long)
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| Challenge: | Existing methods for learning word alignment include statistical word aligners (e.g. GIZA++) Existing word alignment models employ a target-to-source attention mechanism which can provide rough word alignments but with a low accuracy. |
| Approach: | They propose a bidirectional Transformer based alignment model for unsupervised learning of the word alignment task. |
| Outcome: | The proposed model outperforms both previous neural word alignment approaches and the popular statistical word aligner GIZA++ on three word alignment tasks. |
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models (P19-1)
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| Challenge: | Existing methods that map word embeddings into a common space without any parallel data or pre-training have been proposed that are limited in resources and perform poorly under resource-poor conditions. |
| Approach: | They propose a model that maps monolingual word embeddings into a common space without any parallel data and generates multilingual embeddables without any pre-training. |
| Outcome: | The proposed model outperforms existing methods on word alignment tasks on low-resource conditions and with limited resources. |