seq2seq chatbot links
Links to the implementations of neural conversational models for different frameworks. Contributions are welcomed.
A dialog system that is able to express emotions in a text conversation. See online demo.
Sample results for neutral emotions:
hi → hey you how are you ? → i ' m fine , what about you ? how old are you ? → i ' m 16 , how old are you ? are you a leader or a follower ? → i ' m a man who is a politician . my name is david . what is my name ? → i love your name . is the sky blue or black ? → the sky is blue you love anime . → i ' m not a weeb
Hi → Hi. What is your name ? → Laura. What does that mean ? → I dunno. How old are you ? → thirty-five. Who is Laura ? → My brother. Say 'goodbye' → Alright. Two plus two → manny...
Modified code of machine translation model. No answering randomisation is implemented in this code, so the models answers with the same phrase way each time:
hello baby → hello how old are you ? → twenty . i am lonely → i am not nice → you ' re not going to be okay . so rude → i ' m sorry . are you a robot or human? → no . are you better than siri? → yes .
Source code for a bunch of super nice articles by Jiwei Li - one of my favorite researches in the domain of neural conversational systems. Worth checking out.
tell me ... how long have you had this falling sickness ? → a few months, I guess . so i had the doctors test sammy ’s response to conditioning . → so he took the pills . what are you going to do ? → i ’m going to get you a new car . they fear your power your intellect . → i ’m not afraid of your power .
Hello? → Hi. How are you? → I'm fine. What's your name? → It's hard to describe. How so? → I'm not sure. What color is the sky? → It's blue. What is your job? → It's not that i'm a fucking werewolf!
The most popular implementation of Seq2seq architecture on GitHub. However, there is still no evidence of getting good results with this repo.
A wrapper for farizrahman4u/seq2seq used for running experiments. No good results were achieved so far.
Need to check this up.
A new generative chatbot whose training converges in few epochs, including a model pre-trained on a small but consistent dataset collected from dialogues of English courses online.
user: Hello! How are you? computer: i am fine , ok , well , thank you . (with probability of 0.540239) user: What's your name? computer: janet . (with probability of 0.319482) user: What do you like to do now? computer: let me think a minute . (with probability of 0.286760) user: I want to chat about computer science. computer: i am glad to hear there . (with probability of 0.293063) user: Are you a real girl or a computer program? computer: i am self employed . i would love to be well . (with probability of 0.026870) user: Why don't you look for a job? computer: i do not know . i am the bank . (with probability of 0.053494) user: So, you don't need a job! computer: no . i have to make a lot of money . (with probability of 0.001279)
A great overview of existing conversational datasets.
Get a lot of raw movie subtitles (~1.2Gb)
~ 40Mb after clearing out the technical data.
Refer to this great review by Richard Krisztian Csaky: Chatbot and Related Research Paper Notes with Images
as well as to his report paper in pdf: Deep Learning Based Chatbot Models