Neurosymbolic Programming (NP) techniques have the potential to accelerate
scientific discovery. These models combine neural and symbolic components to
learn complex patterns and representations from data, using high-level concepts
or known constraints. NP techniques can interface with symbolic domain
knowledge from scientists, such as prior knowledge and experimental context, to
produce interpretable outputs. We identify opportunities and challenges between
current NP models and scientific workflows, with real-world examples from
behavior analysis in science: to enable the use of NP broadly for workflows
across the natural and social sciences.

Neurosymbolic Programming techniques combine neural and symbolic components to learn complex patterns from data, and can interface with prior knowledge to produce interpretable outputs, providing opportunities for scientific discovery.

科学的神经符号编程

Neurosymbolic Programming for Science

The neural attention model has achieved great success in data-to-text
generation tasks. Though usually excelling at producing fluent text, it suffers
from the problem of information missing, repetition and "hallucination". Due to
the black-box nature of the neural attention architecture, avoiding these
problems in a systematic way is non-trivial. To address this concern, we
propose to explicitly segment target text into fragment units and align them
with their data correspondences. The segmentation and correspondence are
jointly learned as latent variables without any human annotations. We further
impose a soft statistical constraint to regularize the segmental granularity.
The resulting architecture maintains the same expressive power as neural
attention models, while being able to generate fully interpretable outputs with
several times less computational cost. On both E2E and WebNLG benchmarks, we
show the proposed model consistently outperforms its neural attention
counterparts.

本文提出了一种基于神经注意力模型的新方法，利用分段单元和隐变量技术来解决现有的信息缺失，重复和 “幻觉” 问题，减少计算成本，成功地在数据到文本生成任务中实现更好的性能。

通过联合学习分割和对应实现神经数据到文本生成

Neural Data-to-Text Generation via Jointly Learning the Segmentation and  Correspondence

Applying machine learning in the health care domain has shown promising
results in recent years. Interpretable outputs from learning algorithms are
desirable for decision making by health care personnel. In this work, we
explore the possibility of utilizing causal relationships to refine diagnostic
prediction. We focus on the task of diagnostic prediction using discomfort
drawings, and explore two ways to employ causal identification to improve the
diagnostic results. Firstly, we use causal identification to infer the causal
relationships among diagnostic labels which, by itself, provides interpretable
results to aid the decision making and training of health care personnel.
Secondly, we suggest a post-processing approach where the inferred causal
relationships are used to refine the prediction accuracy of a multi-view
probabilistic model. Experimental results show firstly that causal
identification is capable of detecting the causal relationships among
diagnostic labels correctly, and secondly that there is potential for improving
pain diagnostics prediction accuracy using the causal relationships.

本篇论文研究了利用因果关系来改进医疗领域中的疾病诊断预测模型，通过因果关系的推断提高医生的决策能力和模型的预测准确性。