| Challenge: | Existing approaches to deal with noisy multimodal inputs are not robust enough to deal effectively with noisy data. |
| Approach: | They propose a method that composes domain adapters to deal with noisy inputs . they combine these adapters at runtime via dynamic routing or when source of noise is unknown . |
| Outcome: | The proposed model is flexible and state-of-the-art to deal with noisy multimodal inputs. |
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Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation (D19-55)
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| Challenge: | Neural Machine Translation models are sensitive to noise in the input data. |
| Approach: | They propose new methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. |
| Outcome: | The proposed methods extend limited noisy data and improve robustness to noise while keeping the models small. |
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation? (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on bilingual machine translation with a single translation direction. |
| Approach: | They propose a robustness transfer analysis protocol to analyze the transferability of robustness across different languages in multilingual neural machine translation. |
| Outcome: | The proposed protocol shows that the robustness gained in one translation direction can transfer to other translation directions. |
Visual Cues and Error Correction for Translation Robustness (2021.findings-emnlp)
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| Challenge: | Existing robustness techniques fail when faced with unseen types of noise and their performance degrades on clean texts. |
| Approach: | They propose visual context to improve translation robustness for noisy texts . they also propose an error correction training regime that can be used as an auxiliary task . |
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Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing studies on multi-modal neural machine translation focus on visual information, but text and image may not match exactly, and visual noise is often ignored. |
| Approach: | They propose a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT. |
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PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
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Improving Both Domain Robustness and Domain Adaptability in Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to domain adaptation for NMT depend on high-quality parallel data. |
| Approach: | They propose a meta-learning framework which improves domain robustness and adaptability . they use a word-level domain mixing model and a domain classifier to integrate it . |
| Outcome: | The proposed approach improves domain robustness and adaptability in seen and unseen domains. |
Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages (L18-1)
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| Challenge: | Using multilingual and multi-way neural machine translation approaches is a major advantage . training NMT systems for individual language pairs takes significantly more time than training of SMT systems . |
| Approach: | They propose to employ multilingual and multi-way neural machine translation approaches for morphologically rich languages such as Estonian and Russian. |
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Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)
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| Challenge: | Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed . |
| Approach: | They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input. |
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Improving Robustness of Machine Translation with Synthetic Noise (N19-1)
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| Challenge: | Recent work on MT robustness has demonstrated the need to build or adapt systems that are resilient to such noise. |
| Approach: | They propose to synthesize natural noise in social media data to enhance robustness of MT systems by leveraging natural noise. |
| Outcome: | The proposed method can make a vanilla MT system more resilient to noise, partially mitigating loss in accuracy resulting therefrom. |
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (P19-1)
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| Challenge: | Neural machine translation models are sensitive to noises in input sentences . one special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. |
| Approach: | They propose to embed phonetic and textual information into neural machine translation datasets to improve robustness to homophone noises. |
| Outcome: | The proposed method improves the robustness of neural machine translation to homophone noises on clean test sets. |