Cognitive task analysis (CTA) is a type of analysis in applied psychology
aimed at eliciting and representing the knowledge and thought processes of
domain experts. In CTA, often heavy human labor is involved to parse the
interview transcript into structured knowledge (e.g., flowchart for different
actions). To reduce human efforts and scale the process, automated CTA
transcript parsing is desirable. However, this task has unique challenges as
(1) it requires the understanding of long-range context information in
conversational text; and (2) the amount of labeled data is limited and
indirect---i.e., context-aware, noisy, and low-resource. In this paper, we
propose a weakly-supervised information extraction framework for automated CTA
transcript parsing. We partition the parsing process into a sequence labeling
task and a text span-pair relation extraction task, with distant supervision
from human-curated protocol files. To model long-range context information for
extracting sentence relations, neighbor sentences are involved as a part of
input. Different types of models for capturing context dependency are then
applied. We manually annotate real-world CTA transcripts to facilitate the
evaluation of the parsing tasks

本文介绍了一种弱监督的信息提取框架，用于自动化的认知任务分析（CTA）转录解析，将解析过程分为序列标记任务和文本跨度对关系提取任务，并应用了不同类型的模型来捕捉上下文依赖性。

从专家那里搜集知识：用于认知任务分析的自动转录解析

Eliciting Knowledge from Experts:Automatic Transcript Parsing for  Cognitive Task Analysis

This paper offers a multi-disciplinary review of knowledge acquisition
methods in human activity systems. The review captures the degree of
involvement of various types of agencies in the knowledge acquisition process,
and proposes a classification with three categories of methods: the human
agent, the human-inspired agent, and the autonomous machine agent methods. In
the first two categories, the acquisition of knowledge is seen as a cognitive
task analysis exercise, while in the third category knowledge acquisition is
treated as an autonomous knowledge-discovery endeavour. The motivation for this
classification stems from the continuous change over time of the structure,
meaning and purpose of human activity systems, which are seen as the factor
that fuelled researchers' and practitioners' efforts in knowledge acquisition
for more than a century.
We show through this review that the KA field is increasingly active due to
the higher and higher pace of change in human activity, and conclude by
discussing the emergence of a fourth category of knowledge acquisition methods,
which are based on red-teaming and co-evolution.

本文综述了人类活动系统中的知识获取方法，并提出了三类方法的分类：人力代理、人类启发代理和自主机器代理方法。作者们指出，这一分类的动机源于人类活动系统的结构、含义和目的随时间的不断变化，这也推动了研究人员和实践者对知识获取的努力。最后作者们讨论了基于红队和共同进化的知识获取方法的出现。