This project explored the effect of the dataset and the learning rate and the number of hidden layers of the model on chatbot performance. We trained our Chatbot on Cornell Movie Corpus which contains 220,579 conversational exchanges between 10,292 pairs of movie characters, Daily Dialog Corpus which contains 13,118 multi-turn dialogues, and the mix of Cornell Movie Corpus and Daily Dialog Corpus. To improve the performance of the Chatbot, we trained a Dialog Act Classifier to label Cornell Movie Corpus. Then add the Dialog Act as a feature to train the Chatbot. We evaluated the performance of Chatbot in grammaticality, naturalness, and interestingness.
Sequence to Sequence model trained on cornell movie dataset and daily dialogue dataset.
You: hi.
ChatBot: I am not sure I am not going to be aout.
You: how are you.
ChatBot: I am not sure I am not sure.
You: Where are you from?
ChatBot: I am not sure.
You: What do you do for a living?
ChatBot: I am not sure I am not going to be aout.
You: Can you let me know what time it is?
ChatBot: I am not sure.
You: Do you have time to go shopping tomorrow?
ChatBot: I am not sure I am a littleout.
You: When is your birthday?
ChatBot: I am not sure I am not going to be able to be aout.
You: Which one do you prefer, a dog or a cat?
ChatBot: I am not sure I am not going to be able to be aout.
You: When is the next Olympic?
ChatBot: I am not sure.
You: Can you recommend any country to go on a trip?
ChatBot: I am not sure.
You: How is the weather today?
ChatBot: I am not sure I am not going to be aout.
You: I miss my family.
ChatBot: I am not sure.
You: have a good day.
ChatBot: I am not sure I am not sure I am not going to be able to be aout.
You: happy new year.
ChatBot: I am not sure I am not going to be able to be able to be aout.