| Challenge: | Existing models for translating a sentence in a text do not consider coreference relations provided within the text. |
| Approach: | They propose a graph-based encoder which can consider coreference relations provided within the text explicitly. |
| Outcome: | The proposed model improves on the previous approach by 0.9 points on the BLEU score . the graph-based encoder can handle a longer text well, compared with the previous model . |
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| Challenge: | despite well-reasoned intuitions, most context-aware neural machine translation models show only modest improvements over sentence-level systems. |
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Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks (N18-2)
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| Challenge: | Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. |
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Context-Aware Neural Machine Translation Decoding (D19-65)
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| Challenge: | Existing approaches to enhance neural machine translation systems to take into account document-level information make the training process slower or require document- level annotated data. |
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Document Graph for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Standard machine translation systems process sentences in isolation and ignore extra-sentential information. |
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Incorporating Centering Theory into Neural Coreference Resolution (2022.naacl-main)
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| Challenge: | Recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. |
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Analysing Coreference in Transformer Outputs (D19-65)
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| Challenge: | Using a transformer architecture, we study coreference phenomena in three neural machine translation systems. |
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Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have used Graph Neural Networks (GNNs) to encode language knowledge into token embeddings. |
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Document Context Neural Machine Translation with Memory Networks (P18-1)
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| Challenge: | Experimental results show that our model exploits both source and target document context. |
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A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation (2020.acl-main)
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