| Challenge: | Empirical experiments on Chinese-to-English and Japanese-to English datasets show that the proposed attention model delivers significant improvements in terms of alignment error rate and BLEU. |
| Approach: | They propose to explicitly access the target foresight word in the attention model to improve alignment and translation accuracy. |
| Outcome: | Empirical results show that the proposed model improves alignment error rate and BLEU on Chinese-to-English and Japanese-toEnglish datasets. |
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| Challenge: | Multilingual machine translation is a task of building a system capable of translating between multiple source and target languages. |
| Approach: | They propose task-specific attention models to retain parameter sharing generalization . they observe improved translation quality even in low-resource zero-shot directions . |
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A Visual Attention Grounding Neural Model for Multimodal Machine Translation (D18-1)
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| Challenge: | Existing approaches to multimodal machine translation do not integrate visual information into the translation process. |
| Approach: | They propose a multimodal machine translation model that utilizes parallel visual and textual information. |
| Outcome: | The proposed model outperforms existing methods on the Multi30K and Ambiguous COCO datasets. |
Sparse and Constrained Attention for Neural Machine Translation (P18-2)
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| Challenge: | Existing approaches to address coverage problem only change attention transformations . adequacy of neural machine translation is still a major concern . |
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Enhancing Machine Translation with Dependency-Aware Self-Attention (2020.acl-main)
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| Challenge: | Currently, most neural machine translation models rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. |
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| Challenge: | Attention-based neural machine translation models selectively focus on specific source positions to produce a translation. |
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Document-Level Neural Machine Translation with Hierarchical Attention Networks (D18-1)
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| Challenge: | Neural machine translation (NMT) can be improved by including document-level contextual information. |
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Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing studies show that a conventional visual attention mechanism trained in an unsupervised manner is not effective for multimodal neural machine translation. |
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Neural Machine Translation with Decoding History Enhanced Attention (C18-1)
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On the Word Alignment from Neural Machine Translation (P19-1)
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| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
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