| Challenge: | Recent advances in deep learning have boosted the development of neural machine translation (NMT). |
| Approach: | They propose a flow-adapter architecture for unsupervised neural machine translation that leverages normalizing flows to model distributions of sentence-level latent representations. |
| Outcome: | The proposed model achieves competitive results on several unsupervised MT benchmarks. |
Similar Papers
Variational Neural Machine Translation with Normalizing Flows (2020.acl-main)
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| Challenge: | Existing frameworks for learning informative latent variables are limited by limitations . existing models rely on strong assumptions on distribution of latent code . |
| Approach: | They propose to apply a variational neural machine translation framework to a Transformer . they propose to introduce a more flexible approximate posterior based on normalizing flows . |
| Outcome: | The proposed framework outperforms baseline models under in-domain and out-of-domain conditions. |
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. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
Simple, Scalable Adaptation for Neural Machine Translation (D19-1)
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| Challenge: | Recent advances in deep learning have led to significantly improved quality on Neural Machine Translation (NMT) however, performance on out-of-domain data or low resource languages remains poor. |
| Approach: | They propose a simple yet efficient approach for adapting pre-trained models to multiple tasks simultaneously. |
| Outcome: | The proposed approach is on par with full fine-tuning on domain adaptation and massively multilingual NMT on a massively multilingual dataset. |
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
| Approach: | They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models. |
| Outcome: | The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache. |
Learning Source Phrase Representations for Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing approaches to machine translation have been shown to be effective for long sentences . however, the attentional network can't capture long-distance dependencies . |
| Approach: | They propose a multi-head attention mechanism which generates phrase representations from token representations and incorporates them into the Transformer translation model to enhance its ability to capture long-distance relationships. |
| Outcome: | The proposed model can be computed in parallel and improves on the WMT 14 tasks. |
Multilingual Neural Machine Translation (2020.coling-tutorials)
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| 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. |
Our Neural Machine Translation Systems for WAT 2019 (D19-52)
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| Challenge: | In the last five years, statistical machine translation is gradually fading out in favor of neural machine translation. |
| Approach: | They describe a novel Neural Machine Translation (NMT) system for the WAT 2019 translation tasks they focus on. |
| Outcome: | The proposed system improves translation accuracy while replacing absolute position representations with relative positions. |
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |
FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture (2025.findings-emnlp)
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| Challenge: | Using deep learning to detect malware has attracted great attention due to its notable performance. |
| Approach: | a new approach uses Neural Machine Translation and Normalizing Flows to apply deep learning to malware detection. |
| Outcome: | The proposed approach reduces the burden of data collection by enabling malware detection across multiple ISAs. |