| Challenge: | Experimental results show that deep training is 1:4 faster than training from scratch. |
| Approach: | They propose a shallow-to-deep training method that learns deep models by stacking shallow models. |
| Outcome: | The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks. |
Similar Papers
Learning Deep Transformer Models for Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Neural machine translation models have advanced the previous state-of-the-art by learning mappings between sequences via neural networks and attention mechanisms. |
| Approach: | They propose to use layer normalization to pass the combination of previous layers to the next layer to improve the model. |
| Outcome: | The proposed model outperforms the shallow Transformer-Big/Base baseline model on English-German and Chinese-English tasks by 0.4-2.4 BLEU points. |
Depth Growing for Neural Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
| Approach: | They propose a two-stage approach with three specially designed components to construct deeper NMT models. |
| Outcome: | The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks. |
Training Deeper Neural Machine Translation Models with Transparent Attention (D18-1)
Copied to clipboard
| Challenge: | Existing NMT models are shallow in comparison to convolutional models used for both text and vision tasks. |
| Approach: | They propose to modify the attention mechanism to ease the optimization of deeper models by a simple modification to the seq2seq with attention paradigm. |
| Outcome: | The proposed model achieves consistent gains of 0.7-1.1 BLEU on the benchmark WMT’14 English-German and WMT'15 Czech-English tasks. |
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation (2022.findings-acl)
Copied to clipboard
| Challenge: | Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks. |
| Approach: | They propose to deepen the decoder layer in a Transformer model to reduce the difficulty of deep learning. |
| Outcome: | The proposed method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance. |
Exploiting Sentential Context for Neural Machine Translation (P19-1)
Copied to clipboard
| 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. |
Multiscale Collaborative Deep Models for Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Neural machine translation models with deeper neural networks are difficult to train. |
| Approach: | They propose a MultiScale Collaborative framework to boost gradient back-propagation . they let each encoder block learn a fine-grained representation and enhance it . |
| Outcome: | The proposed framework outperforms baseline models on translation tasks with three translation directions and achieves a BLEU score of 30.56 on the English-to-German task. |
Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent work in multilingual translation has improved translation quality surpassing bilingual baselines using deep transformer models with increased capacity. |
| Approach: | They propose a deep encoder with multiple shallow decoders to reduce inference latency while maintaining translation quality. |
| Outcome: | The proposed model achieves 1.8x speedup on average compared to a standard transformer model with no drop in translation quality. |
DEEP: DEnoising Entity Pre-training for Neural Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Earlier named entity translation methods focus on phonetic transliteration, which ignores the sentence context for translation. |
| Approach: | They propose a DEnoising Entity Pre-training method that leverages monolingual data and a knowledge base to improve named entity translation accuracy within sentences. |
| Outcome: | The proposed method improves on three language pairs and denoising auto-encoding baselines. |
Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to train character-level models require very deep architectures that are difficult and slow to train. |
| Approach: | They propose to fine tune a Transformer token-based model to get a model without token segmentation. |
| Outcome: | The proposed model improves translation quality and robustness to noise while requiring less token segmentation. |
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
Copied to clipboard
| Challenge: | Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings. |
| Approach: | They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data. |
| Outcome: | The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data. |