Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Table 3: Ablation study of our model components. ✓means the corresponding column component is used. ✗means do not use the corresponding column component. Specifically, “Copy” means using copy mechanism, “EF” means using embedding factorization, “OT” means using optimal transport constraint loss, “N” means extracting nouns from both the table and text, and “W” means using the whole table and text to compute OT. Lastly, “latent” means using latent similarity loss.
['Copy', 'EF', 'OT (N/W)', 'latent', 'BLEU', 'METEOR', 'ROUGE', 'PARENT', 'PARENT-T', 'params']
We also conduct extensive ablation studies to better understand each component of our model, including the copy mechanism, embedding factorization, optimal transport constraint loss, and latent similarity loss.