Papers with supervised translation
End-to-End Neural Word Alignment Outperforms GIZA++ (2020.acl-main)
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| Challenge: | Word alignment was once a core unsupervised learning task in natural language processing . but word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. |
| Approach: | They propose to use a Transformer model to train an unsupervised word alignment model. |
| Outcome: | The proposed method outperforms GIZA++ on three data sets and is tightly integrated and does not affect translation quality. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)
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| Challenge: | Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model. |
| Approach: | They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders. |
| Outcome: | The proposed model improves zero-shot translation while maintaining performance in supervised translation on multilingual datasets. |
Program Translation via Code Distillation (2023.emnlp-main)
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| Challenge: | Software version migration and program translation are costly parts of the lifecycle of large codebases. |
| Approach: | They propose a model that captures semantic and structural equivalence of code in a language agnostic intermediate representation. |
| Outcome: | The proposed model achieves state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks translation benchmarks. |