# Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models

arXiv: https://arxiv.org/abs/2107.07119 · HTML: https://mlsanity.com/paper/2107.07119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://mlsanity.com/paper/1909.00204/) (2019)
- [Deep-FSMN for Large Vocabulary Continuous Speech Recognition](https://mlsanity.com/paper/1803.05030/) (2018)
- [Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms](https://mlsanity.com/paper/2005.07845/) (2020)
- [Multimodal Learning For Classroom Activity Detection](https://mlsanity.com/paper/1910.13799/) (2019)
- [Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes](https://mlsanity.com/paper/2006.01204/) (2020)
- [Representation Learning from Limited Educational Data with Crowdsourced Labels](https://mlsanity.com/paper/2009.11222/) (2020)
- [A Multimodal Alerting System for Online Class Quality Assurance](https://mlsanity.com/paper/1909.11765/) (2019)

## Built on by (most cited first, 1 of 1)

- [Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes](https://mlsanity.com/paper/2207.10645/) (2022)

## Abstract (arXiv)

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines. To encourage reproducible results, we make our implementation online available at urlhttps://github.com/AIED2021/multitask-dialogic-instruction.