DeepAI AI Chat
Log In Sign Up

Exploring Semi-Supervised Learning for Predicting Listener Backchannels

by   Vidit Jain, et al.

Developing human-like conversational agents is a prime area in HCI research and subsumes many tasks. Predicting listener backchannels is one such actively-researched task. While many studies have used different approaches for backchannel prediction, they all have depended on manual annotations for a large dataset. This is a bottleneck impacting the scalability of development. To this end, we propose using semi-supervised techniques to automate the process of identifying backchannels, thereby easing the annotation process. To analyze our identification module's feasibility, we compared the backchannel prediction models trained on (a) manually-annotated and (b) semi-supervised labels. Quantitative analysis revealed that the proposed semi-supervised approach could attain 95 revealed that almost 60 predicted by the proposed model more natural. Finally, we also analyzed the impact of personality on the type of backchannel signals and validated our findings in the user-study.


page 5

page 10


Semi-Supervised Machine Learning: a Homological Approach

In this paper we describe the mathematical foundations of a new approach...

Life is not black and white – Combining Semi-Supervised Learning with fuzzy labels

The required amount of labeled data is one of the biggest issues in deep...

A Topological Approach for Semi-Supervised Learning

Nowadays, Machine Learning and Deep Learning methods have become the sta...

Bayesian Methods for Semi-supervised Text Annotation

Human annotations are an important source of information in the developm...

Importance of user inputs while using incremental learning to personalize human activity recognition models

In this study, importance of user inputs is studied in the context of pe...

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases

Pathologists find tedious to examine the status of the sentinel lymph no...

IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection

In this work, we present our approach and findings for SemEval-2021 Task...