| Challenge: | Recent work in context-aware NMT considers only a few previous sentences as context . current systems fail to achieve fluent, good quality translation for a full document . |
| Approach: | They propose a top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context. |
| Outcome: | The proposed approach outperforms context-agnostic baselines and context-based baselines on English-German datasets. |
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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. |
| Approach: | They propose a hierarchical attention model that captures document-level contextual information and conditioning on the NMT model’s own hidden states. |
| Outcome: | The proposed model improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods and that both the encoder and decoder benefit from context in complementary ways. |
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)
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| Challenge: | Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. |
| Approach: | They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation. |
| Outcome: | The proposed model can be used to translate both sentences and documents on four translation tasks. |
On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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| Challenge: | Document-level neural machine translation models produce a more consistent output across a document . however, the exact decoding strategy is often not described and not mentioned at all. |
| Approach: | They propose to use standard automatic metrics and specific linguistic phenomena to compare different decoding schemes. |
| Outcome: | The proposed decoding strategies perform similar to each other on three standard document-level translation benchmarks. |
Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)
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| Challenge: | Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers. |
| Approach: | They evaluate a recent linear attention model with a sentential gate to promote a recency inductive bias and compare it to open-source document translation. |
| Outcome: | The proposed model significantly improves translation quality on IWSLT 2015 and OpenSubtitles 2018 with similar or better BLEU scores. |
Exploiting Sentential Context for Neural Machine Translation (P19-1)
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| Challenge: | Existing approaches to exploit sentential context for machine translation are not well studied. |
| Approach: | They propose a shallow sentential context that exploits top encoder layer, and a deep sentential one that aggregates sentential representations from all internal layers. |
| Outcome: | The proposed model outperforms the strong Transformer model on the English-German and English-French benchmarks. |
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. |
When and Why is Document-level Context Useful in Neural Machine Translation? (D19-65)
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| Challenge: | Recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. |
| Approach: | They extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document- level context in NMT. |
| Outcome: | The proposed model is not interpretable as utilizing the context, and a long context is not helpful for NMT. |
Data augmentation using back-translation for context-aware neural machine translation (D19-65)
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| Challenge: | A single sentence does not always convey information that is enough to translate it into other languages. |
| Approach: | They obtain large-scale pseudo parallel corpora by back-translating monolingual data and examine their impact on translation accuracy. |
| Outcome: | The large-scale pseudo parallel corpora obtained by back-translating monolingual data showed that the model trained with small parallel corporeals and large-sized pseudo parallels improved translation accuracy. |
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. |