Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation (N19-1)
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| Challenge: | Existing work has addressed this problem by leveraging monolingual or multilingual data. |
| Approach: | They propose to introduce a differentiable reconstruction loss for neural machine translation to exploit the limited amounts of parallel text available in low-resource settings. |
| Outcome: | The proposed approach achieves small but consistent BLEU improvements on four language pairs in both translation directions and outperforms an alternative differentiable reconstruction strategy based on hidden states. |
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Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
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| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
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Handling Syntactic Divergence in Low-resource Machine Translation (D19-1)
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| Challenge: | Existing approaches to neural machine translation (NMT) are dependent on limited parallel data, and can be difficult to use for many language pairs. |
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Exploring Recombination for Efficient Decoding of Neural Machine Translation (D18-1)
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Alignment verification to improve NMT translation towards highly inflectional languages with limited resources (2021.eacl-main)
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| Challenge: | Existing approaches to improve translation quality using limited training data are phrase-based and syntax-based approaches. |
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