Papers with WMT14 English-German
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation (2020.acl-main)
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| Challenge: | masked language models have been used for natural language processing tasks but few studies have adopted it in the sequence-to-sequence models. |
| Approach: | They propose to combine encoder and decoder to train a masked sequence-to-sequence model . they propose to train the encoder more rigorously by masking the encoded input . |
| Outcome: | The proposed model achieves 27.69/32.24 BLEU scores on English-German/German-English tasks with 5+ times speed up compared with an autoregressive model. |
Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
| Approach: | They propose a capsule network with dynamic routing for linear time Neural Machine Translation . they map the source sentence into a matrix with pre-determined size and apply a deep LSTM network to decode the target sequence from the source representation. |
| Outcome: | The proposed network achieves comparable results with the Transformer system on English-German and English-French tasks. |
Neural Machine Translation with Decoding History Enhanced Attention (C18-1)
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| Challenge: | Neural machine translation with source-side attention has been criticized for its poor memory performance. |
| Approach: | They propose to use a Decoding History Enhanced Attention mechanism to render NMT models better at selecting both source-side and target-side information. |
| Outcome: | The proposed model improves by 0:9 BLEU on Chinese-English translation and the state-of-the-art on a larger task. |
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to improve neural machine translation use token-level adaptive training . however, standard models make predictions on condition of previous contexts . |
| Approach: | They propose a target-context-aware metric which can be supplemented by statistical metrics . they propose an adaptive training approach based on token- and sentence-level CBMI . |
| Outcome: | The proposed model outperforms the Transformer baseline and other similar approaches on English-German and Chinese-English tasks. |
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation (2022.acl-long)
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| Challenge: | Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation models, but it can lose important information for translating low-frequency words. |
| Approach: | They propose a knowledge distillation method which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data. |
| Outcome: | Extensive experiments on eight WMT benchmarks show that monolingual KD outperforms the standard KD by improving low-frequency word translation without introducing any computational cost. |
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Large-scale training datasets make training neural machine translation models difficult. |
| Approach: | They propose to identify inactive training examples which contribute less to the model performance and introduce data rejuvenation to improve NMT models' training. |
| Outcome: | The proposed framework stabilizes and accelerates the training process of NMT models, resulting in models with better generalization capability. |
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)
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| Challenge: | Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity . |
| Approach: | They propose a multi-candidate optimization framework for diverse NMT to deal with this defect. |
| Outcome: | The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality. |
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation (2021.acl-long)
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| Challenge: | Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation models. |
| Approach: | They propose to use knowledge distillation to generate training data for non-autoregressive translation models by leveraging pretraining. |
| Outcome: | The proposed approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets. |
Multi-Head Attention with Disagreement Regularization (D18-1)
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| Challenge: | Existing methods to encourage diversity among multi-head attention are limited. |
| Approach: | They propose a disagreement regularization term to encourage diversity among attention heads . they validated their approach on EnglishGerman and ChineseEnglish translation tasks . |
| Outcome: | The proposed approach improves translation performance across language pairs on English-German and Chinese-English translation tasks. |
A Study of Reinforcement Learning for Neural Machine Translation (D18-1)
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| Challenge: | Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation systems. |
| Approach: | They propose to leverage reinforcement learning to boost the performance of NMT systems trained with monolingual data. |
| Outcome: | The proposed method achieves competitive results on translation tasks in English-German, Chinese-English and English-English systems. |
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure. |
| Approach: | They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy. |
| Outcome: | The proposed method improves translation performance and robustness to noise on three benchmarks. |
Exploiting Deep Representations for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models typically implement encoder and decoder as multiple layers, but only the top layers are leveraged in the subsequent process, which misses the opportunity to exploit useful information embedded in other layers. |
| Approach: | They propose to expose all of these signals with layer aggregation and multi-layer attention mechanisms and introduce an auxiliary regularization term to encourage different layers to capture diverse information. |
| Outcome: | The proposed approach exposes all of these signals with layer aggregation and multi-layer attention mechanisms on widely-used translation datasets. |
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. |