| Challenge: | Existing systems that combine translation memory and statistical machine translation (MT) models are able to translate less familiar phrases and sentences without sacrificing quality. |
| Approach: | They propose to combine translation memory and Neural Machine Translation (NMT) models to select final translation outputs when similarity score of a test source sentence exceeds the predefined threshold. |
| Outcome: | The proposed system significantly improves translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. |
Similar Papers
NTT Neural Machine Translation Systems at WAT 2019 (D19-52)
Copied to clipboard
| Challenge: | We submitted two systems for scientific paper subtask and timely disclosure subtask . we evaluated the usefulness of incorporating external data from a wide variety of web pages to improve the translation quality. |
| Approach: | They describe two different translation tasks submitted to WAT 2019 . they submitted scientific paper subtasks and timely disclosure subtask . |
| Outcome: | The proposed system performed better on scientific paper and timely disclosure subtasks. |
Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
Copied to clipboard
| 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. |
Fast and Accurate Neural Machine Translation with Translation Memory (2021.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge demonstrates the superiority of TM-based neural machine translation only on TM specialized tasks . |
| Approach: | They propose a translation memory-based approach to machine translation using a single bilingual sentence as its TM. |
| Outcome: | The proposed approach surpasses baselines on two general tasks and improves on the TM-specialized translation tasks. |
Encoding Gated Translation Memory into Neural Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Neural machine translation (MT) technology has made significant progress in the past few years. |
| Approach: | They propose a method to combine the strengths of TM and neural machine translation (NMT) they use a gating mechanism to balance the impact of the TM match on the NMT decoder . |
| Outcome: | The proposed method improves translation quality by over 10 BLEU points when fuzzy matches are higher than 50% on the UN corpus. |
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages (D19-1)
Copied to clipboard
| Challenge: | Using parallel corpora, we train a single, direct NMT model for non-English language pairs. |
| Approach: | They propose three ways to increase the relation among source, pivot, and target languages in pre-training . they use additional adapter component to smoothly connect pre-trained encoder and decoder . |
| Outcome: | The proposed methods outperform multilingual models up to +2.6% BLEU in WMT 2019 French-German and German-Czech tasks. |
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation (P18-2)
Copied to clipboard
| Challenge: | Existing methods to train neural machine translation (NMT) use a fixed training procedure where each sentence is sampled once during each epoch. |
| Approach: | They propose to dynamically sample sentences to accelerate NMT training . a weight is assigned to each sentence based on the measured difference between training costs of two iterations. |
| Outcome: | Empirical results show that the proposed method can significantly accelerate training and improve NMT performance. |
Guiding Neural Machine Translation with Retrieved Translation Pieces (N18-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) has trouble with lowfrequency words or phrases and generalizing across domains. |
| Approach: | They propose a method for recalling low-frequency words and phrases into neural machine translation by retrieving n-grams from a search engine and incorporating them into the decoding process. |
| Outcome: | The proposed method improves translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. |
One Sentence One Model for Neural Machine Translation (L18-1)
Copied to clipboard
| 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. |
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)
Copied to clipboard
Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang
| Challenge: | Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency. |
| Approach: | They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible. |
| Outcome: | The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets. |
Multilingual Neural Machine Translation (2020.coling-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
| Outcome: | This tutorial will cover the latest advances in NMT to enhance low-resource translation models. |