Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
Table 5: BLEU scores (%) of model ablation.
['[EMPTY]', 'Dev', 'Test']
We examine the necessity and the benefit of introducing operations by removing the argmax operation (see “OpAtt Comparing to Seq2Seq+copy, the results show that our full model and “OpAtt w/o argmax op” which incorporates results of operations both work well in terms of BLEU, and the improvements increase with the number of operations. The quantization layer maps the numerical execution results into several bins to enable different lexical choices according to different quantities of values. We design a gating mechanism to decide when to incorporate the results of operations to guide the process of generation. The performance of this ablation is worse than our full model, which demonstrates that the gating mechanism is an essential component. Fig. We can see that the gate weights are reasonable, as the gate values are large when deciding the team leader “horford” and the winner of the game “hawks”.