Papers with WMT’14 English-to-German
Multi-Unit Transformers for Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that the MUTE models outperform the Transformer-Base by up to +1.52, +1.99 and +1.00 BLEU points, with only a mild drop in inference speed (about 3.1%). |
| Approach: | They propose to use multiple parallel units to promote the expressiveness of the Transformer by introducing diverse and complementary units. |
| Outcome: | The proposed models outperform the Transformer-Base model with only a mild drop in inference speed (about 3.1%). |
Recurrent Positional Embedding for Neural Machine Translation (D19-1)
Copied to clipboard
| Challenge: | Existing translation systems that use positional embeddings only encode static order dependencies based on discrete numerical information, which may hinder the improvement of translation capacity. |
| Approach: | They propose a recurrent positional embedding approach based on word vectors that are learned by a neural network and integrated into existing multi-head self-attention models. |
| Outcome: | The proposed approach improves translation performance over the state-of-the-art Transformer baseline in English-to-German and NIST Chinese-to English translation tasks. |
Neural Machine Translation with Reordering Embeddings (P19-1)
Copied to clipboard
| Challenge: | Existing work exploits the reordering information in neural machine translation . experimental results show that the proposed methods can significantly improve the performance of the transformer translation system. |
| Approach: | They propose a reordering mechanism to learn the re ordering embedding of a word based on contextual information and stack them together with self-attention networks to learn sentence representation for machine translation. |
| Outcome: | The proposed method improves translation performance on English-to-German, NIST Chinese-to English, and WAT Japanese-toEnglish translation tasks. |
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models. |
| Approach: | They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words . |
| Outcome: | The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores. |