Papers with Neural machine translation systems
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
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| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning (2021.findings-acl)
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Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Yuqing Tang, Benjamin Rubinstein, Trevor Cohn
| Challenge: | Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, they are also vulnerable to training attacks. |
| Approach: | They propose a poisoning attack in which a malicious adversary inserts a small poisoned sample of monolingual text into a training set of a system trained using back-translation. |
| Outcome: | The proposed attack is based on two methods that can be used to craft poisoned examples. |
Multi-layer Representation Fusion for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation systems require a number of stacked layers for deep models, but the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. |
| Approach: | They propose a multi-layer representation fusion approach to fusing stacked layers to learn a better representation from the stack. |
| Outcome: | The proposed approach yields 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. |
A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems. |
| Approach: | They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric. |
| Outcome: | The proposed paradigm produces stable attacks with meaning-preserving adversarial examples. |
Context and Copying in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation systems with subword vocabularies can translate or copy unknown words . we examine the influence of context and subword features on copying behavior . |
| Approach: | They show that neural machine translation systems with subword vocabularies can translate unknown words . they also learn to copy words based on context and features of the words themselves . |
| Outcome: | The proposed model outperforms phrase-based statistical machine translation systems on translation of unknown words. |