| Challenge: | Existing models for Neural Machine Translation (NMT) use Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. |
| Approach: | They propose a Neural Machine Translation (NMT) model that decodes the sequence with the guidance of its structural prediction of the target-side context. |
| Outcome: | The proposed model is more competitive compared with the state-of-the-art methods and reduces repetition with the instruction from the target-side context for decoding. |
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A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
| Approach: | They propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side. |
| Outcome: | The proposed model outperforms standard seq2seq models by 2.1 BLEU points and other methods for incorporating target-side syntax by 0.7 BLUE points. |
Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Outcome: | Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets. |
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing (D18-1)
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| Challenge: | Neural machine translation (NMT) models are based on sequential decoding or serialisation of structured data into sequence. |
| Approach: | They propose a model that combines sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. |
| Outcome: | The proposed model produces fluent translations with better reordering than previous models. |
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT (2021.emnlp-main)
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| Challenge: | Statistical MT decomposes the translation task into distinct components that are learned separately. |
| Approach: | They show that neural machine translation models acquire different competences over the course of training . previous work shows how to improve some of the competences in NMT by using lexical translation probabilities, phrase memories, alignment information. |
| Outcome: | The proposed model improves translation quality and word-by-word translation, while learning complex reordering patterns. |
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies on self-supervised pretraining for machine translation have focused on the jointly pretrained decoder . |
| Approach: | They propose a method to improve neural machine translation by jointly pretrained decoder . they propose two strategies to remedy the domain and objective discrepancies . |
| Outcome: | The proposed approach improves translation performance and model robustness on three language pairs. |
One Sentence One Model for Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) is a new state of the art that can produce better results than traditional statistical machine translation. |
| Approach: | They propose a dynamic neural network which learns a general network as usual and fine-tunes it for each test sentence. |
| Outcome: | The proposed method improves translation performance when similar sentences are available. |
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation (D18-1)
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| Challenge: | Existing models treat source and target sentences as one-dimensional sequences over time, while a 2D mapping is achieved using an MDLSTM layer. |
| Approach: | They propose a multi-dimensional long short-term memory architecture for translation modelling that uses an MDLSTM layer to define the correspondence between source and target words. |
| Outcome: | The proposed model improves on two WMT 2017 tasks, showing that the source and target sentences are aligned with each other in a 2D grid. |
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)
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| Challenge: | Despite the success of neural machine translation, simultaneous neural machine translators are challenging due to syntactic structure difference and simultaneity requirements. |
| Approach: | They propose a framework for adapting neural machine translation to translate simultaneously . they propose 'prefix translation' that utilizes a consecutive NMT model to translate source prefixes . |
| Outcome: | The proposed framework balancing quality and latency on three translation corpora and two language pairs shows that it performs well. |
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. |
Exploring Recombination for Efficient Decoding of Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) decoder captures features of entire prediction history . some partial hypotheses with different prefixes will be regarded differently no matter how similar they are . |
| Approach: | They propose a method that uses a n-gram suffix to adapt it to beam search decoding. |
| Outcome: | The proposed method can obtain similar translation quality with a smaller beam size, making it more efficient. |