Papers with multi-encoder approaches
Sequence Shortening for Context-Aware Machine Translation (2024.findings-eacl)
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| Challenge: | Context-aware Machine Translation aims to improve translations of sentences by incorporating surrounding sentences as context. |
| Approach: | They propose to use latent representation of source sentence as context in a multi-encoder architecture to achieve higher accuracy on contrastive datasets. |
| Outcome: | The proposed architectures achieve comparable BLEU and COMET scores on contrastive datasets and comparable accuracies on the single- and multi-encoder approaches. |
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)
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| Challenge: | In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence. |
| Approach: | They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences. |
| Outcome: | The proposed approach encodes the context and the current sentence without contexts. |