Machine Translation Pre-training for Data-to-Text Generation - A Case Study in Czech
Table 5: Human evaluations for accuracy and fluency
['model', 'accuracy ↑', 'fluency ↑']
We conduct accuracy and fluency evaluations for our best model (binmt) and the best lexicalized baseline, mass. In terms of fluency, we note that mass produces quite fluent text, likely due to its strong language model. It would seem that unsupervised learning on unlabeled data is enough to generate fluent text, echoing findings of past work binmt performs slightly better with a score of 4.83. However, when it comes to accuracy, our model gets a high score of 97.5, surpassing mass by 7.5 points. We take this as proof that transfer learning from machine translation helps produce text that is not only fluent, but much better grounded in the structured data.