| Challenge: | Experimental results show that our model exploits both source and target document context. |
| Approach: | They propose a document-level neural machine translation model which takes both source and target document context into account using memory networks. |
| Outcome: | The proposed model outperforms previous work in terms of BLEU and METEOR in English translations. |
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Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model (2021.naacl-main)
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| Challenge: | Neural machine translation models that incorporate inter-sentential contexts can be trained only in document-level parallel data with sentential alignments. |
| Approach: | They propose a method to perform context-aware decoding with any pre-trained translation model . their method uses sentence-level parallel data and target-side document-level monolingual data . |
| Outcome: | The proposed method performs context-aware decoding on English to Russian translation using BLEU and contrastive tests. |
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation (2022.findings-naacl)
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| Challenge: | Recent studies have shown that the effective use of contextual information between sentences can achieve better performance in document-level machine translation. |
| Approach: | They propose a recurrent memory unit to the Transformer to support the information exchange between the sentence and previous context. |
| Outcome: | The proposed model outperforms the previous work on TED and News by 0.91 s-BLEU and 1.49 d-BLUE on average. |
Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
| Approach: | They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly . |
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Challenges in Context-Aware Neural Machine Translation (2023.emnlp-main)
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| Challenge: | despite well-reasoned intuitions, most context-aware neural machine translation models show only modest improvements over sentence-level systems. |
| Approach: | They propose a more realistic setting for document-level translation called paragraph-to-paragraph (PARA2PARA) they collect a dataset of Chinese-English novels to promote future research . |
| Outcome: | The proposed model improves translation quality across document-level metrics and discourse phenomena. |
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. |
| Approach: | They propose a decoding architecture that fuses the semantic space language model and a neural translation model. |
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Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
Corpora for Document-Level Neural Machine Translation (2020.lrec-1)
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| Challenge: | Document-level machine translation models translate sentences in isolation, but there are three main problems for document-level models. |
| Approach: | They propose to use document-level machine translation to capture discourse dependencies across sentences by considering a document as a whole. |
| Outcome: | The proposed method captures discourse dependencies across sentences by considering a document as a whole. |
Context-Interactive Pre-Training for Document Machine Translation (2021.naacl-main)
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| Challenge: | Document machine translation typically suffers from a lack of document-level bilingual data. |
| Approach: | They propose a document machine translation model that incorporates contextual information into the training signals by capturing cross-sentence dependency within the target document and cross sentence translation to make better use of contextual information. |
| Outcome: | The proposed model outperforms baselines on three benchmark datasets and significantly outperformed previous approaches. |
Document Graph for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing document-level NMT methods fail to leverage contexts beyond a few set of previous sentences. |
| Approach: | They propose to represent a document as a graph that connects relevant contexts regardless of distances. |
| Outcome: | Experiments on IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian show that using document graphs can significantly improve translation quality. |
Document Sub-structure in Neural Machine Translation (2020.lrec-1)
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| Challenge: | Current approaches to machine translation (MT) translate sentences in isolation, disregarding context they appear in, or model context at the level of the full document. |
| Approach: | They propose to include information about the topic of the section within which each sentence is found in a document that is not homogeneous . they use a cache-based model to model the context of the document, instead of translating sentences in isolation . |
| Outcome: | The proposed model incorporates information about the topic of the section within which each sentence is found into a neural model. |