Project Background:
The dialogue systems center around developing artificial intelligence systems that engage in natural language conversations with users. Dialogue systems, or conversational agents or chatbots, have become increasingly prevalent in various domains, including customer service, healthcare, education, and entertainment. The goal is to create intelligent systems that can understand user queries, respond appropriately, and maintain coherent and contextually relevant conversations over time. This field has seen significant advancements driven by breakthroughs in natural language processing, machine learning, and deep learning techniques. Key objectives include improving the robustness and naturalness of conversational agents, enhancing their ability to handle complex dialogue flows and understanding user intent accurately. Additionally, research in dialogue systems often delves into sentiment analysis, emotion recognition, and personalized interaction to create more engaging and empathetic conversational experiences.
Problem Statement
- Develop techniques to accurately interpret user utterances, including intents, entities, and contextual cues, to understand user queries in natural language.
- Improve the ability of dialogue systems to generate coherent and contextually relevant responses that effectively address user queries and maintain the flow of conversation.
- Develop personalized methods based on user preferences, history, and demographic information, enabling tailored and engaging interactions.
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Explore methods to enhance user engagement and satisfaction by creating more empathetic, responsive, and human-like conversational experiences.
- Develop scalable systems that handle large interactions across diverse domains and deployment scenarios, including web, mobile, and conversational interfaces.
Aim and Objectives
- Develop advanced dialogue systems for engaging and natural language-based interactions with users.
- Enhance natural language understanding to interpret user queries accurately.
- Improve response generation for coherent and contextually relevant interactions.
- Personalize dialogue systems for tailored and engaging user experiences.
- Address ambiguity and uncertainty in user queries and system responses.
- Explore multimodal integration for richer interactions.
- Enhance user engagement and satisfaction through empathetic interactions.
- Develop scalable systems for deployment across diverse platforms.
- Define standardized evaluation metrics and benchmarks for assessing system performance.
Contributions to Dialogue Systems
- Novel natural language understanding techniques for accurate interpretation of user queries.
- Advanced response generation methods for coherent and contextually relevant interactions.
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Personalization strategies to tailor dialogue systems to individual user preferences and characteristics.
- Techniques for handling ambiguity and uncertainty in user queries and system responses.
- Integration of multiple modalities for richer and more expressive interactions.
- Insights into enhancing user engagement and satisfaction through empathetic dialogue design.
- Scalable system architectures for deployment across various platforms and domains.
Deep Learning Algorithms for Dialogue Systems
- Sequence-to-Sequence (Seq2Seq) models
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Transformer-based models (BERT, GPT, T5)
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory (LSTM) networks
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Attention Mechanisms
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Variational Autoencoders (VAEs)
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Hierarchical Recurrent Encoder-Decoder (HRED) models
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Generative Pre-trained Transformer (GPT) models
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Pre-trained language models (BERT, RoBERTa)
Datasets for Dialogue Systems
- Cornell Movie-Dialogs Corpus
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Ubuntu Dialogue Corpus
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Twitter Dialogue Corpus
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DailyDialog Dataset
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PersonaChat Dataset
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OpenSubtitles Dataset
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MultiWOZ Dataset
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DSTC (Dialog State Tracking Challenge) Datasets
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Yelp Restaurant Reviews Dataset
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Reddit Dialogue Corpus
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
