| Challenge: | Unsupervised neural machine translation (NMT) is a popular method for transferring information between languages. |
| Approach: | They propose an unsupervised pivot translation method which translates a language to a distant language through multiple hops. |
| Outcome: | The proposed method improves translation on 20 languages and 294 distant languages on 20 different languages and language pairs. |
Similar Papers
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages (D19-1)
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| 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. |
Visual Pivoting Unsupervised Multimodal Machine Translation in Low-Resource Distant Language Pairs (2024.findings-emnlp)
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| Challenge: | Existing studies show that neural MT achieves much worse translation quality than statistical MT with a small number of corpora. |
| Approach: | They propose a visual pivoting method for alignment between distant language pairs . they first construct a dataset and then apply it to pre-training and fine-tuning . |
| Outcome: | The proposed method outperforms baselines on DLPs and close language pairs. |
An Effective Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only. |
| Approach: | They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems. |
| Outcome: | The proposed system achieves 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more in the (supervised) shared task winner back in 2014. |
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. |
Neural Machine Translation between Low-Resource Languages with Synthetic Pivoting (2024.lrec-main)
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| Challenge: | Pivot-based neural machine translation systems overcome data scarcity by including a high-resource pivot language in the process of translating between low-resourced languages. |
| Approach: | They propose a novel approach to pivot-based translation in which pivot sentences are generated synthetically from both the source and target languages. |
| Outcome: | The proposed approach improves pivot-based systems translating between low-resource Southern African languages by up to 5.6 BLEU points after fine-tuning. |
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance (D18-1)
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| Challenge: | a framework for cross-domain and cross-language transfer has hardly been explored . cross-linguistic and cross language transfer methods are used for multilingual applications . |
| Approach: | They propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks and bilingual word embeddings to train a model on labeled data from one language pair. |
| Outcome: | The proposed model outperforms existing models even when trained in the lazy setup . the proposed model can be applied to nine English-German and nine English - french domain pairs without retraining . |
Unsupervised Extraction of Partial Translations for Neural Machine Translation (N19-1)
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| Challenge: | Neural machine translation systems usually require a large quantity of bilingual parallel data for training. |
| Approach: | They propose an algorithm for extracting from monolingual data what they call partial translations . partial translation is a pair of source and target sentences that contain sequences of tokens that are translations of each other. |
| Outcome: | The proposed algorithm extracts from monolingual data what we call partial translations . it takes only source and target monolingual datasets as input . |
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables (2021.findings-emnlp)
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| Challenge: | Existing methods to achieve zero-shot translation suffer from spurious correlations between output language and language invariant semantics. |
| Approach: | They propose a method that denoizes the autoencoder objective based on pivot language into traditional training objective to improve translation accuracy on zero-shot directions. |
| Outcome: | The proposed method eliminates spurious correlations and outperforms state-of-the-art methods on two benchmark machine translation datasets. |
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. |
Exploring Unsupervised Pretraining Objectives for Machine Translation (2021.findings-acl)
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| Challenge: | Unsupervised cross-lingual pretraining has significantly reduced the need for large parallel data. |
| Approach: | They compare unsupervised cross-lingual pretraining with masking and reconstructing inputs in the decoder to produce real sentences. |
| Outcome: | The proposed methods produce inputs resembling real (full) sentences, by reordering and replacing words based on their context. |