Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis
Abstract
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 realworld CTA transcripts to facilitate the evaluation of the parsing tasks1.