Open Access
Table 3
Overall comparison of Concepts from Literature Survey.
| Model | Advantages | Suited problem statements | Chatbot applications using the model |
|---|---|---|---|
| ANN [17–19] | – Self-organizing to changes in information – Fault-tolerant to the corruption of cells and missing input values |
ANN is well suited for classification and regression dealing with a large number of variables. Character recognition, Image processing are some of the applications for ANN | eHealth Chatbot, Tourism Chatbot, Quiz Generation and Answering Chatbot |
| RNN [20–22] | – Adapts wells to quick changes in the input nodes – Variable size of input and output vectors – Works well with contextual input sequences – Excel at modelling temporal structure |
Appropriate for sequence prediction, classification prediction, and Natural language processing and generative model. Hence they can be used in text generation, prediction of the values of an attribute in a problem statement. | Combating Depression, Emotion Recognition, Technical Support Automation |
| Ensemble Learning [23–25] | – Better generalization ability – Weak models can be boosted to efficient learners – Because of growing computations power, the Ensemble model can be well utilized |
It can be used to enhance the performance of existing models like RNN, LSTM and GRU. | Diabetes Personalised Treatment Chatbot, Fashion Recommender Chatbot |
| LSTM [26–28] | – Extended memory capabilities than RNN – Handles Long-term dependencies well. – More robust to vanishing gradients that RNN |
The right choice for problem statements like Time series forecasting. LSTM can be applied in Conversation agent, handwriting generation, Language translation, Image captioning. | Twitterbot, AgriBot, Midoriko Anime Chatbot |
| GRU [29,30] | – Handles Long-term dependencies effectively – Robust to Vanishing gradient problem – Computationally effective than LSTM |
GRU can be used in applications related to time series prediction like text generations, classification, etc | Cultural Heritage website chatbot, Papaya English dialogue chatbot |
| NTM [31,32] | – Generalize well to longer inputs as compared to LSTM – Presence of external memory complements the RNNs existing memory |
NTM is well suited for models with massive and more extended sequences of data. NTM has demonstrated the solutions to be generalizing well for basic algorithms like copying and sorting. | Agnostic Chatbot, Arabic Chatbot |
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