Papers with reconstruction training
Tiny Word Embeddings Using Globally Informed Reconstruction (2020.coling-main)
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| Challenge: | Existing methods for word embedding reconstruction use only local information of subwords and pre-trained word embeds. |
| Approach: | They propose a global loss function that uses words other than the target word to improve word embedding reconstruction by a factor of 200. |
| Outcome: | The proposed method reduces the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. |
Reference Language based Unsupervised Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Existing approaches to use a common language as an auxiliary for better translation have a long tradition in machine translation. |
| Approach: | They propose a reference language-based framework for unsupervised neural machine translation that uses only one auxiliary language as an auxiliary for better translation. |
| Outcome: | The proposed framework improves the quality of pivot translation over a baseline that uses only one auxiliary language. |