One Source, Two Targets: Challenges and Rewards of Dual Decoding (2021.emnlp-main)
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| Challenge: | Neural Machine Translation (NMT) is progressing at a rapid pace. |
| Approach: | They propose to combine two outputs so that each side depends on the other . they highlight the challenges of dual decoding and analyze the benefits of generating matched, rather than independent, translations. |
| Outcome: | The proposed system can generate matched, rather than independent, translations. |
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
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On Decoding Strategies for Neural Text Generators (2022.tacl-1)
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| Challenge: | a recent study suggests that decoding strategies may be more important than the model architecture itself when generating text from probabilistic models. |
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Twist Decoding: Diverse Generators Guide Each Other (2022.emnlp-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
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| Challenge: | Experimental results show that multilingual NMT models handle multiple language pairs in one model. |
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Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)
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| Challenge: | State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. |
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Revisiting Multi-Domain Machine Translation (2021.tacl-1)
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| Challenge: | Existing approaches to handle multi-domain machine translation systems are lacking due to the variability of data. |
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Leveraging Synthetic Targets for Machine Translation (2023.findings-acl)
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| Challenge: | Using synthetic target data, training models on synthetic targets outperforms training on actual ground-truth data. |
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The Source-Target Domain Mismatch Problem in Machine Translation (2021.eacl-main)
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Jiajun Shen, Peng-Jen Chen, Matthew Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc’Aurelio Ranzato
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