Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)
Table 2: Automatic evaluation results on test set. Results were obtained using Wiseman’s trained extractive evaluation models with relexicalization Li and Wan (2018). ∗ We include delayed copy (DEL)’s result in the paper Li and Wan (2018) for comparison.
['[BOLD] Model', '[BOLD] RG  [BOLD] P%', '[BOLD] RG  [BOLD] #', '[BOLD] CS  [BOLD] P%', '[BOLD] CS  [BOLD] R%', '[BOLD] CO  [BOLD] DLD%', '[BOLD] BLEU']
Row, column and time dimension information are important to the modeling of tables because subtracting any of them will result in performance drop. Also, position embedding is critical when modeling time dimension information according to the results. In addition, record fusion gate plays an important role because BLEU, CO, RG P% and CS P% drop significantly after subtracting it from full model. Results show that each component in the model contributes to the overall performance. Wiseman et al. Puduppully et al. \newciteLi ’s model generate a template at first and then fill in the slots with delayed copy mechanism. Since its result in \newciteLi’s paper was evaluated by IE model trained by \newciteWiseman and “relexicalization” by \newciteLi, we adopted the corresponding IE model and re-implement “relexicalization” as suggested by \newciteLi for fair comparison. Please note that CC’s evaluation results via our re-implemented “relexicalization” is comparable to the reported result in \newciteLi. ’s paper.