Hybrid tabular-textual question answering (QA) requires reasoning from
heterogeneous information, and the types of reasoning are mainly divided into
numerical reasoning and span extraction. Despite being the main challenge of
the task compared to extractive QA, current numerical reasoning method simply
uses LSTM to autoregressively decode program sequences, and each decoding step
produces either an operator or an operand. However, the step-by-step decoding
suffers from exposure bias, and the accuracy of program generation drops
sharply with progressive decoding. In this paper, we propose a
non-autoregressive program generation framework, which facilitates program
generation in parallel. Our framework, which independently generates complete
program tuples containing both operators and operands, can significantly boost
the speed of program generation while addressing the error accumulation issue.
Our experiments on the MultiHiertt dataset shows that our model can bring about
large improvements (+7.97 EM and +6.38 F1 points) over the strong baseline,
establishing the new state-of-the-art performance, while being much faster
(21x) in program generation. The performance drop of our method is also
significantly smaller than the baseline with increasing numbers of numerical
reasoning steps.

通过提出一种非自回归程序生成框架，可以同时独立生成包含运算符和操作数的完整程序元组，从而显着提高程序生成速度，同时解决了误差积累问题，实验结果显示在 MultiHiertt 数据集上，我们的模型比强基准模型有很大的改进（+7.97EM 和 + 6.38F1 分），并且在程序生成方面速度更快（21 倍），同时我们的方法在增加数值推理步骤的情况下性能的下降也显着小于基线模型。