Recurrent Neural Networks (RNNs): RNNs are very popular for natural language processing tasks like chatbots as they can learn long-term dependencies in sequential data like text. Some common RNN variants used for chatbots include –
Long Short Term Memory (LSTM) networks: LSTM networks are a type of RNN that is well-suited for learning from experiences (e.g. large amounts of conversational data). They can capture long-term dependencies better than traditional RNNs as they avoid the vanishing gradient problem. LSTM networks have memory cells that allow them to remember inputs for long periods of time. This ability makes them very useful for modeling sequential data like natural language. LSTM based chatbots can retain contextual information from previous sentences or turns in a conversation to have more natural and coherent dialogues.
Gated Recurrent Unit (GRU) networks: GRU is another type of RNN architecture proposed as a simplification of LSTM. Like LSTMs, GRUs have gating units that allows them to learn long-term dependencies. However, GRUs have fewer parameters than LSTMs, making them faster to train and requiring less computational resources. For some tasks, GRUs have been shown to perform comparable to or even better than LSTMs. GRU based models are commonly used for chatbots, particularly for resource constrained applications.
Bidirectional RNNs: Bidirectional RNNs use two separate hidden layers – one processes the sequence forward and the other backward. This allows the model to have access to both past and future context at every time step. Bidirectional RNNs have been shown to perform better than unidirectional RNNs on certain tasks like part-of-speech tagging, chunking, name entity recognition and language modeling. They are widely used as the base architectures for developing contextual chatbots.
Convolutional Neural Networks (CNNs): Just like how CNNs have been very successful in computer vision tasks, they have also found use in natural language processing. CNNs are able to automatically learn hierarchical representations and meaningful features from text. They have been used to develop various natural language models for classification, sequence labeling etc. CNN-RNN combinations have also proven very effective for tasks involving both visual and textual inputs like image captioning. For chatbots, CNNs pre-trained on large unlabeled text corpora can help extract highly representative semantic features to power conversations.
Transformers: Transformers like BERT, GPT, T5 etc. based on the attention mechanism have emerged as one of the most powerful deep learning architectures for NLP. The transformer encoder-decoder architecture allows modeling of both the context and the response in a conversation without relying on sequence length or position information. This makes Transformers very well-suited for modeling human conversations. Contemporary chatbots are now commonly built using large pre-trained transformer models that are further fine-tuned on dialog data. Models like GPT-3 have shown very human-like capabilities for open-domain question answering without any hand-crafted rules or additional learning.
Deep reinforcement learning models: Deep reinforcement learning provides a way to train goal-driven agents through rewards and punishment signals. Models like the deep Q-network (DQN) can be used to develop chatbots that learn successful conversational strategies by maximizing long-term rewards through dialog simulations. Deep reinforcement agents can learn optimal policies to decide the next action (like responding appropriately, asking clarifying questions etc.) based on the current dialog state and history. This allows developing goal-oriented task-based chatbots with skills that humans can train through samples of ideal and failed conversations. The models get better through practice by trial-and-error without being explicitly programmed.
Knowledge graphs and ontologies: For task-oriented goal-driven chatbots, static knowledge bases defining entities, relations, properties etc. has proven beneficial. Knowledge graphs represent information in a graph structure where nodes denote entities or concepts and edges indicate relations between them. Ontologies define formal vocabularies that help chatbots comprehend domains. Connecting conversations to a knowledge graph using NER and entity linking allows chatbots to retrieve and internally reason over relevant information, aiding responses. Knowledge graphs guide learning by providing external semantic priors which help generalize to unseen inputs during operation.
Unsupervised learning techniques like clustering help discover hidden representations in dialog data for use in response generation. This is useful for open-domain settings where labeled data may be limited. Hybrid deep learning models combining techniques like RNNs, CNNs, Transformers, RL with unsupervised learning and static knowledge graphs usually provide the best performances. Significant progress continues to be made in scaling capabilities, contextual understanding and multi-task dialogue with the advent of large pre-trained language models. Chatbot development remains an active research area with new models and techniques constantly emerging.