https://github.com/achrefbenammar404/quasi-patterned-conversations-analysis
Official Implementation of the IEEE EUROCON 2025 Paper A Computational Approach to Modeling Conversational Systems Analyzing Large-Scale Quasi-Patterned Dialogue Flows Mohamed Achref Ben Ammar – National Institute of Applied Science and Technology (INSAT), University of Carthage, Tunisia Mohamed Taha Bennani – University of Tunis El Manar (FST)
https://github.com/achrefbenammar404/quasi-patterned-conversations-analysis
ai computational-linguistics conversational-agent conversational-ai data-analysis graph-algorithms nlp research research-paper
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Official Implementation of the IEEE EUROCON 2025 Paper A Computational Approach to Modeling Conversational Systems Analyzing Large-Scale Quasi-Patterned Dialogue Flows Mohamed Achref Ben Ammar – National Institute of Applied Science and Technology (INSAT), University of Carthage, Tunisia Mohamed Taha Bennani – University of Tunis El Manar (FST)
- Host: GitHub
- URL: https://github.com/achrefbenammar404/quasi-patterned-conversations-analysis
- Owner: achrefbenammar404
- License: mit
- Archived: true
- Created: 2024-08-20T02:26:18.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-15T21:07:57.000Z (about 1 year ago)
- Last Synced: 2025-07-26T14:52:30.674Z (11 months ago)
- Topics: ai, computational-linguistics, conversational-agent, conversational-ai, data-analysis, graph-algorithms, nlp, research, research-paper
- Language: Python
- Homepage:
- Size: 2.84 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
A Computational Approach to Modeling Conversational Systems
Analyzing Large-Scale Quasi-Patterned Dialogue Flows
Official Implementation of the IEEE EUROCON 2025 Paper
A Computational Approach to Modeling Conversational Systems
Analyzing Large-Scale Quasi-Patterned Dialogue Flows
Mohamed Achref Ben Ammar – National Institute of Applied Science and Technology (INSAT), University of Carthage, Tunisia
Mohamed Taha Bennani – University of Tunis El Manar (FST)
---
## Abstract
The rise of large language models (LLMs) has led to increasingly complex and loosely structured dialogues. In this work, we introduce a **computational graph-based framework** that models these quasi-patterned conversations. Central to our approach is the **Filter & Reconnect** method, a graph simplification technique that reduces conversational noise while preserving semantic structure.
Key outcomes:
- **2.06× improvement in semantic metric S** over prior methods
- **0 δ-hyperbolicity**, enforcing a tree-like, interpretable structure
This framework offers practical tools for monitoring and analyzing chatbot behavior, dialogue management systems, and user interaction patterns at scale.
---
## Methodology Overview
The methodology consists of the following core steps:
1. **Utterance Extraction**
Conversational utterances are extracted from a structured dataset consisting of multi-turn dialogues.
2. **Semantic Embedding**
Each utterance is transformed into a dense vector using a pre-trained text embedding model, capturing the semantic meaning of the message.
3. **Clustering of Intents**
Using hierarchical clustering techniques and a large language model (LLM), similar utterances are grouped together to identify key communicative intents.
4. **Markov Chain Construction**
A Markov Chain is built where nodes represent clustered intents and edges represent transitions between them in the dialogue flow.
5. **Graph Simplification: Filter & Reconnect**
The conversational graph undergoes a noise reduction process by removing irrelevant transitions while preserving semantic and structural coherence.
6. **Flow Pattern Analysis**
The resulting graph is then analyzed to identify quasi-patterned conversational flows, enabling improved interpretability and dialogue system evaluation.
---
## Setup
### 1. Install Dependencies
```bash
# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate # Linux/MacOS
venv\Scripts\activate # Windows
# Install required packages
pip install -r requirements.txt
# Download required NLP model
python -m spacy download en_core_web_md
```
### 2. Create a `.env` File
At the project root, create a `.env` file and configure the following environment variables:
```dotenv
# Python setup
PYTHONPATH=${PYTHONPATH}:.
# Environment mode
ENVIRONMENT="local"
# API Keys
GOOGLE_API_KEY=
MISTRAL_API_KEY=
```
> Ensure your API keys are valid and have the appropriate access privileges.
---
## Input Data Format
This framework supports **ABCD v1.1**, **MultiWOZ 2.0**, or any **custom dataset** formatted as follows:
```json
{
"conversation_1": [
{"role": "agent", "content": "Hello, how can I help you today?"},
{"role": "customer", "content": "I need assistance with my account."},
{"role": "action", "content": "Agent opened account details."}
]
}
```
- Save your data file as: `data/processed_formatted_conversations.json`
---
## Run the Pipeline
```bash
python main.py \
--file_path data/processed_formatted_conversations.json \
--num_sampled_data 500 \
--min_clusters 10 \
--max_clusters 30 \
--model_name 'sentence-transformers/all-mpnet-base-v2' \
--label_model 'open-mixtral-8x22b' \
--tau 0.15 \
--top_k 2 \
--alpha 0.8
```
---
## Advanced Configuration
| Parameter | Description | Default |
|----------------------|--------------------------------------------------|---------------------------------|
| `--num_sampled_data` | Number of conversations to sample | 100 |
| `--min_clusters` | Minimum cluster count for elbow method | 5 |
| `--max_clusters` | Maximum cluster count for elbow method | 15 |
| `--model_name` | Sentence embedding model | 'all-MiniLM-L12-v2' |
| `--label_model` | LLM for labeling dialogue state clusters | 'open-mixtral-8x22b' |
| `--tau` | Minimum transition probability threshold | 0.1 |
| `--top_k` | Number of outgoing edges to retain per node | 1 |
| `--alpha` | Balance between semantic similarity and topology | 1.0 |
---
## Citation
If you use this codebase for your research, please cite:
```bibtex
@inproceedings{achref2025conversationalgraph,
title={A Computational Approach to Modeling Conversational Systems: Analyzing Large-Scale Quasi-Patterned Dialogue Flows},
author={Mohamed Achref Ben Ammar and Mohamed Taha Bennani},
conference={IEEE EUROCON 2025 - The 21st International Conference on Smart Technologies},
year={2025},
publisher={IEEE},
}
```
---
## Contact
For questions, collaborations, or feedback, feel free to reach out:
- **Mohamed Achref Ben Ammar** – [mohamedachref.benammar@insat.ucar.tn](mailto:mohamedachref.benammar@insat.ucar.tn)
- **Mohamed Taha Bennani** – [taha.bennani@fst.utm.tn](mailto:taha.bennani@fst.utm.tn)