# Exploring Semi-Supervised Learning for Predicting Listener Backchannels

arXiv: https://arxiv.org/abs/2101.01899 · HTML: https://mlsanity.com/paper/2101.01899/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [Cross-Modal Data Programming Enables Rapid Medical Machine Learning](https://mlsanity.com/paper/1903.11101/) (2019)
- [Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor](https://mlsanity.com/paper/1706.01340/) (2017)
- [Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children](https://mlsanity.com/paper/2007.11346/) (2020)
- [Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset for Personality Assessment](https://mlsanity.com/paper/2008.13769/) (2020)

## Built on by (most cited first, 6 of 6)

- [Didn't see that coming: a survey on non-verbal social human behavior forecasting](https://mlsanity.com/paper/2203.02480/) (2022)
- [Yeah, Un, Oh: Continuous and Real-time Backchannel Prediction with Fine-tuning of Voice Activity Projection](https://mlsanity.com/paper/2410.15929/) (2024)
- [Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement](https://mlsanity.com/paper/2409.13726/) (2024)
- [TalkTive: A Conversational Agent Using Backchannels to Engage Older Adults in Neurocognitive Disorders Screening](https://mlsanity.com/paper/2202.08216/) (2022)
- [Modeling Speaker-Listener Interaction for Backchannel Prediction](https://mlsanity.com/paper/2304.04472/) (2023)
- [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026)

## Abstract (arXiv)

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% of the former's performance. Our user-study findings revealed that almost 60% of the participants found the backchannel responses 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.