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Retrieval-Based Conversational Model in Tensorflow is a project implementing a retrieval-based conversational model using a dual LSTM encoder architecture in TensorFlow, illustrating how neural networks can be trained to select appropriate responses from a fixed set of candidate replies rather than generate them from scratch. The core idea is to embed both the conversation context and potential replies into vector representations, then score how well each candidate fits the current dialogue, choosing the best match accordingly. Designed to work with datasets like the Ubuntu Dialogue Corpus, this codebase includes data preparation, model training, and evaluation components for building and assessing dialog models that can handle multi-turn conversations.
Features
- Dual LSTM encoder architecture for context-response matching
- Retrieval-based conversational modeling approach
- TensorFlow implementation with training and evaluation scripts
- Designed for multi-turn dialog datasets like Ubuntu Dialogue Corpus
- Produces ranked candidate responses instead of generative text
- Educational codebase for learning dialog systems
Project Samples
