Papers by Xinwei Geng
Adaptive Multi-pass Decoder for Neural Machine Translation (D18-1)
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| Challenge: | End-to-end neural machine translation (NMT) has attracted increasing attention in recent years. |
| Approach: | They propose an adaptive multi-pass decoder which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning. |
| Outcome: | The proposed architecture improves Chinese-English translation with 1.55 BLEU . the proposed architecture adopts a flexible multi-pass polishing mechanism . |
How Does Selective Mechanism Improve Self-Attention Networks? (2020.acl-main)
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| Challenge: | Experimental results show that selective SANs outperform the standard SAN by paying more attention to content words that contribute to the meaning of the sentence. |
| Approach: | They propose to implement selective SANs with a flexible Gumbel-Softmax to improve word order encoding and structure modeling. |
| Outcome: | The proposed system outperforms the standard SANs on several representative NLP tasks including natural language inference, semantic role labelling, and machine translation. |
Learning to Rewrite for Non-Autoregressive Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing non-autoregressive neural machine translations have poor inference speed but weak recognition of erroneous translation pieces. |
| Approach: | They propose an architecture to explicitly learn to rewrite the erroneous translation pieces. |
| Outcome: | The proposed architecture can achieve better performance while significantly reducing decoding time. |
Unifying the Convergences in Multilingual Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing approaches to multilingual neural machine translation are overfitting and inconsistency is ignored . |
| Approach: | They propose a training strategy that picks up language-specific best checkpoints for each language pair to teach the current model on the fly. |
| Outcome: | The proposed training strategy alleviates convergence inconsistency and achieves state-of-the-art on language pairs. |
Towards Higher Pareto Frontier in Multilingual Machine Translation (2023.acl-long)
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| Challenge: | Existing Pareto optimization approaches are limited by the long-tailed distribution of multilingual corpora. |
| Approach: | They propose a Pareto mutual distillation framework that pushes the Paret frontier outwards rather than making trade-offs. |
| Outcome: | The proposed framework pushes the Pareto frontier outwards rather than making trade-offs, the authors show. |
Improving Non-Autoregressive Neural Machine Translation via Modeling Localness (2022.coling-1)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from poor localization quality due to sequential dependencies within the target sentence. |
| Approach: | They propose to introduce local information into NAT models by explicitly introducing local information about surrounding words into the encoder and decoder sides to achieve localness-aware representations. |
| Outcome: | The proposed method can achieve significant improvements over strong NAT baselines. |