Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation (D18-1)
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| Challenge: | Neural text generation has been quite successful recently, but during training time, only one reference is considered for each example, even though there are often multiple references available. |
| Approach: | They propose an algorithm to generate exponentially many pseudo-references by compressing existing references into lattices and traversing them to generate new pseudo-References. |
| Outcome: | The proposed model significantly improves on baselines in machine translation and image captioning, and is comparable to existing models. |
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