# Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations

arXiv: https://arxiv.org/abs/2111.06647 · HTML: https://mlsanity.com/paper/2111.06647/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations](https://mlsanity.com/paper/2105.09984/) (2021)
- [Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1603.03827/) (2016)
- [DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2008.06914/) (2020)
- [Dialogue Act Classification with Context-Aware Self-Attention](https://mlsanity.com/paper/1904.02594/) (2019)
- [Guiding attention in Sequence-to-sequence models for Dialogue Act prediction](https://mlsanity.com/paper/2002.08801/) (2020)
- [Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction](https://mlsanity.com/paper/2106.03044/) (2021)
- [Dialogue Act Recognition via CRF-Attentive Structured Network](https://mlsanity.com/paper/1711.05568/) (2017)
- [Multi-task dialog act and sentiment recognition on Mastodon](https://mlsanity.com/paper/1807.05013/) (2018)
- [Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer](https://mlsanity.com/paper/2103.12360/) (2021)
- [ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections](https://mlsanity.com/paper/1708.00630/) (2017)
- [Speaker-change Aware CRF for Dialogue Act Classification](https://mlsanity.com/paper/2004.02913/) (2020)
- [Contextual Dialogue Act Classification for Open-Domain Conversational Agents](https://mlsanity.com/paper/2005.13804/) (2020)
- [Modeling Long-Range Context for Concurrent Dialogue Acts Recognition](https://mlsanity.com/paper/1909.00521/) (2019)
- [Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions](https://mlsanity.com/paper/1902.11060/) (2019)

## Built on by (most cited first, 30 of 31)

- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [Towards Human-centered Proactive Conversational Agents](https://mlsanity.com/paper/2404.12670/) (2024)
- [Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations](https://mlsanity.com/paper/2305.10172/) (2023)
- [MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance](https://mlsanity.com/paper/2503.13509/) (2025)
- [Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering](https://mlsanity.com/paper/2206.03886/) (2022)
- [Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations](https://mlsanity.com/paper/2403.02090/) (2024)
- [Towards Online Multi-Modal Social Interaction Understanding](https://mlsanity.com/paper/2503.19851/) (2025)
- [Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling](https://mlsanity.com/paper/2301.12729/) (2023)
- [A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework](https://mlsanity.com/paper/2203.11856/) (2022)
- [Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification](https://mlsanity.com/paper/2501.15321/) (2025)
- [Trust Modeling in Counseling Conversations: A Benchmark Study](https://mlsanity.com/paper/2501.03064/) (2025)
- [We Care: Multimodal Depression Detection and Knowledge Infused Mental Health Therapeutic Response Generation](https://mlsanity.com/paper/2406.10561/) (2024)
- [Omni-MMSI: Toward Identity-attributed Social Interaction Understanding](https://mlsanity.com/paper/2604.00267/) (2026)
- [Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations](https://mlsanity.com/paper/2406.08718/) (2024)
- [Eeyore: Realistic Depression Simulation via Supervised and Preference Optimization](https://mlsanity.com/paper/2503.00018/) (2025)
- [Dynamically Retrieving Knowledge via Query Generation for Informative Dialogue Generation](https://mlsanity.com/paper/2208.00128/) (2022)
- [LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation](https://mlsanity.com/paper/2505.19667/) (2025)
- [PSI-Bench: Interpretable and Clinically Meaningful Evaluation of Depression Patient Simulators](https://mlsanity.com/paper/2604.25840/) (2026)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [EMMI -- Empathic Multimodal Motivational Interviews Dataset: Analyses and Annotations](https://mlsanity.com/paper/2406.16478/) (2024)
- [OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations](https://mlsanity.com/paper/2512.09804/) (2025)
- [Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation](https://mlsanity.com/paper/2604.05795/) (2026)
- [Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations](https://mlsanity.com/paper/2604.18539/) (2026)
- [Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation](https://mlsanity.com/paper/2401.05134/) (2024)
- [SAGE: A Strategy-Aware Graph-Enhanced Generation Framework For Online Counseling](https://mlsanity.com/paper/2604.26630/) (2026)
- [EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation](https://mlsanity.com/paper/2605.22286/) (2026)
- [Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour](https://mlsanity.com/paper/2606.18129/) (2026)
- [GEMCo: A Validated, Ethically Releasable Proxy for Inaccessible Counselling Data](https://mlsanity.com/paper/2607.23621/) (2026)
- [MyMentorLLM: A psychotherapy GenAI environment with multimodal voice/text patients, trainees and experts for deliberate practice](https://mlsanity.com/paper/2607.25667/) (2026)
- [LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues](https://mlsanity.com/paper/2609.03507/) (2026)

## Abstract (arXiv)

The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA.