Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education

by   Debajyoti Datta, et al.

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.


page 1

page 2

page 3

page 4


Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus

Most Semantic Role Labeling (SRL) approaches are supervised methods whic...

Active WeaSuL: Improving Weak Supervision with Active Learning

The availability of labelled data is one of the main limitations in mach...

Bootstrapping Conversational Agents With Weak Supervision

Many conversational agents in the market today follow a standard bot dev...

TagRuler: Interactive Tool for Span-Level Data Programming by Demonstration

Despite rapid developments in the field of machine learning research, co...

Active Learning with Weak Supervision for Cost-Effective Panicle Detection in Cereal Crops

Panicle density of cereal crops such as wheat and sorghum is one of the ...

Evaluation of mathematical questioning strategies using data collected through weak supervision

A large body of research demonstrates how teachers' questioning strategi...

Animating an Autonomous 3D Talking Avatar

One of the main challenges with embodying a conversational agent is anno...

Please sign up or login with your details

Forgot password? Click here to reset