Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
Table 4: BLEU scores (%) over two datasets. Statistical significant is indicated with *(p< 0.05) with respect to Seq2Seq+copy.
['[EMPTY]', 'ESPN Dev', 'ESPN Test', 'ROTOWIRE Dev', 'ROTOWIRE Test']
We adopt both automatic evaluation and human evaluation to evaluate the proposed model. Automatic Evaluation We employ BLEU-4 as the metric for automatic evaluation. Our proposed model OpAtt outperforms neural network baselines See et al. The results show that our method which incorporates the operations enables generating texts that are fidelity to facts and therefore yields the best performance. Seq2Seq+op+quant outperforms the baseline method Seq2Seq+copy, but is not as good as our method. The result confirms that our proposed method with specialized operation encoder and gating mechanism utilizes the information of operations more effectively. Moreover, Seq2Seq+op+quant outperforms Seq2Seq+op showing the effectiveness of the quantization layer.