Question answering requiring discrete reasoning, e.g., arithmetic computing,
comparison, and counting, over knowledge is a challenging task. In this paper,
we propose UniRPG, a semantic-parsing-based approach advanced in
interpretability and scalability, to perform unified discrete reasoning over
heterogeneous knowledge resources, i.e., table and text, as program generation.
Concretely, UniRPG consists of a neural programmer and a symbolic program
executor, where a program is the composition of a set of pre-defined general
atomic and higher-order operations and arguments extracted from table and text.
First, the programmer parses a question into a program by generating operations
and copying arguments, and then the executor derives answers from table and
text based on the program. To alleviate the costly program annotation issue, we
design a distant supervision approach for programmer learning, where pseudo
programs are automatically constructed without annotated derivations. Extensive
experiments on the TAT-QA dataset show that UniRPG achieves tremendous
improvements and enhances interpretability and scalability compared with
state-of-the-art methods, even without derivation annotation. Moreover, it
achieves promising performance on the textual dataset DROP without derivations.

本文提出 UniRPG—— 一种基于 Semantic Parsing 的方法用于 Knowledge 中的离散推理，包括算术计算，比较和计数，以及属于表格和文本等异构知识资源；UniRPG 结合了神经程序员和符号程序执行器来执行程序生成和回答问题。

UniRPG：基于表格和文本的统一离散推理生成程序

UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation

Deep neural networks have achieved impressive supervised classification
performance in many tasks including image recognition, speech recognition, and
sequence to sequence learning. However, this success has not been translated to
applications like question answering that may involve complex arithmetic and
logic reasoning. A major limitation of these models is in their inability to
learn even simple arithmetic and logic operations. For example, it has been
shown that neural networks fail to learn to add two binary numbers reliably. In
this work, we propose Neural Programmer, an end-to-end differentiable neural
network augmented with a small set of basic arithmetic and logic operations.
Neural Programmer can call these augmented operations over several steps,
thereby inducing compositional programs that are more complex than the built-in
operations. The model learns from a weak supervision signal which is the result
of execution of the correct program, hence it does not require expensive
annotation of the correct program itself. The decisions of what operations to
call, and what data segments to apply to are inferred by Neural Programmer.
Such decisions, during training, are done in a differentiable fashion so that
the entire network can be trained jointly by gradient descent. We find that
training the model is difficult, but it can be greatly improved by adding
random noise to the gradient. On a fairly complex synthetic table-comprehension
dataset, traditional recurrent networks and attentional models perform poorly
while Neural Programmer typically obtains nearly perfect accuracy.

本研究提出了一种新颖的神经程序模型（Neural Programmer），该模型扩展了具有少量基本算术和逻辑运算的不可区分神经网络，此模型可以在多个步骤中调用这些扩展操作，因此产生的组成程序比内置操作更复杂。该模型采用相对较弱的监督信号进行训练，不需要昂贵的编写正确程序的注释，经过训练在很复杂的综合表理解数据集中取得了几乎完美的准确度。