Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Classification through Weak Supervision in Context-specific
  Conversational Agent Development for Teacher Education   Machine learning techniques applied to the Natural Language Processing (NLP)
component of conversational agent development show promising results for
improved accuracy and quality of feedback that a conversational agent can
provide. The effort required to develop an educational scenario specific
conversational agent is time consuming as it requires domain experts to label
and annotate noisy data sources such as classroom videos. Previous approaches
to modeling annotations have relied on labeling thousands of examples and
calculating inter-annotator agreement and majority votes in order to model the
necessary scenarios. This method, while proven successful, ignores individual
annotator strengths in labeling a data point and under-utilizes examples that
do not have a majority vote for labeling. We propose using a multi-task weak
supervision method combined with active learning to address these concerns.
This approach requires less labeling than traditional methods and shows
significant improvements in precision, efficiency, and time-requirements than
the majority vote method (Ratner 2019). We demonstrate the validity of this
method on the Google Jigsaw data set and then propose a scenario to apply this
method using the Instructional Quality Assessment(IQA) to define the categories
for labeling. We propose using probabilistic modeling of annotator labeling to
generate active learning examples to further label the data. Active learning is
able to iteratively improve the training performance and accuracy of the
original classification model. This approach combines state-of-the art labeling
techniques of weak supervision and active learning to optimize results in the
educational domain and could be further used to lessen the data requirements
for expanded scenarios within the education domain through transfer learning.

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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: hep-ex