DeepQA - My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

•    Python

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This work tries to reproduce the results of A Neural Conversational Model (aka the Google chatbot). It uses a RNN (seq2seq model) for sentence predictions. It is done using python and TensorFlow. The loading corpus part of the program is inspired by the Torch neuralconvo from macournoyer.

chatbot deep-learning tensorflow seq2seq

neuralconvo - Neural conversational model in Torch

•    Lua

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This is an attempt at implementing Sequence to Sequence Learning with Neural Networks (seq2seq) and reproducing the results in A Neural Conversational Model (aka the Google chatbot). Human: What is the purpose of living? Machine: To live forever.

seq2seq torch machine-learning deep-learning neural-conversation-models

stealth - An open source Ruby framework for conversational voice and text chatbots. 🤖

•    Ruby

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Stealth is a Ruby based framework for creating conversational (voice & chat) bots. It's design is inspired by Ruby on Rails's philosophy of convention over configuration. It has an MVC architecture with the slight caveat that views are aptly named replies. Stealth is extensible. All service integrations are split out into separate Ruby Gems. Things like analytics and natural language processing (NLP) can be added in as gems as well.

chatbot chatbot-framework voice bot bot-framework bots natural-language-processing stealth rails alexa-skill alexa-skills-kit facebook-messenger-bot conversational-ui conversational-bots conversational-agents conversational-ai

Rasa - Create chatbots and voice assistants

•    Python

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Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build chatbots on Facebook, Slack, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram etc. Rasa's primary purpose is to help you build contextual, layered conversations with lots of back-and-forth. To have a real conversation, you need to have some memory and build on things that were said earlier. Rasa lets you do that in a scalable way.

nlp machine-learning machine-learning-library bot bots botkit conversational-bots conversational-agents conversational-ai spacy mitie chatbot chatbots chatbots-framework bot-framework natural-language-processing

DeepPavlov - An open source library for deep learning end-to-end dialog systems and chatbots.

•    Python

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Import key components to build HelloBot. Create skills as pre-defined responses for a user's input containing specific keywords. Every skill returns response and confidence.

bot nlp chatbot dialogue-systems question-answering chitchat slot-filling intent-classification entity-extraction named-entity-recognition keras tensorflow deep-learning deep-neural-networks intent-detection dialogue-agents dialogue-manager

tf_seq2seq_chatbot - [unmaintained]

•    Python

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Note: the repository is not maintained. Feel free to PM me if you'd like to take up the maintainance. Build a general-purpose conversational chatbot based on a hot seq2seq approach implemented in tensorflow. Since it doesn't produce good results so far, also consider other implementations of seq2seq.

seq2seq chatbot tensorflow

seq2seq_chatbot_links - Links to the implementations of neural conversational models for different frameworks

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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.

seq2seq chatbot

RLSeq2Seq - Deep Reinforcement Learning For Sequence to Sequence Models

•    Python

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NOTE: THE CODE IS UNDER DEVELOPMENT, PLEASE ALWAYS PULL THE LATEST VERSION FROM HERE. In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.

reinforcement-learning actor-critic policy-gradient abstractive-text-summarization pointer-generator nlp

botframework-solutions - Welcome to the Bot Framework Solutions repository which is the home for a set of templates and solutions to help build advanced conversational experiences using Azure Bot Service and Bot Framework

•    CSharp

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This repository is the home for a set of templates and solutions to help build conversational experiences using Azure Bot Service and Bot Framework. Things look a little different around here? Find out more in our Wiki page and here.

bot bot-framework azure-bot-service assistant conversation-experiences virtual-assistant conversation conversational-ui conversational-agents conversational-bots conversational-ai conversational-interfaces virtualassistant virtual va skills microsoft

seq2seq - Sequence to Sequence Learning with Keras

•    Python

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Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python deep learning library Keras. Using Seq2Seq, you can build and train sequence-to-sequence neural network models in Keras. Such models are useful for machine translation, chatbots (see [4]), parsers, or whatever that comes to your mind. That's it! You have successfully compiled a minimal Seq2Seq model! Next, let's build a 6 layer deep Seq2Seq model (3 layers for encoding, 3 layers for decoding).

ChatterBot - ChatterBot is a machine learning, conversational dialog engine for creating chat bots

•    Python

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ChatterBot is a machine-learning based conversational dialog engine build in Python which makes it possible to generate responses based on collections of known conversations. The language independent design of ChatterBot allows it to be trained to speak any language. An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with.

chatterbot machine-learning chatbot conversation language bot

seq2seq-couplet - Play couplet with seq2seq model. 用深度学习对对联。

•    Python

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This is a project use seq2seq model to play couplets (对对联)。 This project is written with Tensorflow. You can try the demo at https://ai.binwang.me/couplet. You will need some data to run this program, the dataset can be downloaded from this project.

seq2seq deep-learning machine-learning

seq2seq - Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch

•    Python

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Minimal Seq2Seq model with attention for neural machine translation in PyTorch. This implementation relies on torchtext to minimize dataset management and preprocessing parts.

seq2seq deep-learning machine-translation

TC-Bot - User Simulation for Task-Completion Dialogues

•    OpenEdge

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An implementation of the End-to-End Task-Completion Neural Dialogue Systems and A User Simulator for Task-Completion Dialogues. This document describes how to run the simulation and different dialogue agents (rule-based, command line, reinforcement learning). More instructions to plug in your customized agents or user simulators are in the Recipe section of the paper.

user-simulator nlg dialogue-agents nlu end-to-end

Facebook-Messenger-Bot - Facebook chatbot that I trained to talk like me using Seq2Seq

•    Python

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The FB Messenger chatbot that I trained to talk like me. The associated blog post. For this project, I wanted to train a Sequence To Sequence model on my past conversation logs from various social media sites. You can read more about the motivation behind this approach, the details of the ML model, and the purpose of each Python script in the blog post, but I want to use this README to explain how you can train your own chatbot to talk like you.

chatbot seq2seq-model facebook-messenger-bot tensorflow

nmt-keras - Neural Machine Translation with Keras (Theano/Tensorflow)

•    Python

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Neural Machine Translation with Keras (Theano and Tensorflow). for obtaining the required packages for running this library.

neural-machine-translation keras deep-learning sequence-to-sequence theano machine-learning nmt machine-translation lstm-networks gru tensorflow attention-mechanism web-demo transformer attention-is-all-you-need attention-model attention-seq2seq

debug_seq2seq - [unmaintained] Make seq2seq for keras work

•    Python

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Note: the repository is not maintained. Feel free to PM me if you'd like to take up the maintainance. Make seq2seq for keras work. And also give a try to some other implementations of seq2seq.

seq2seq chatbot keras

seq2seq - A general-purpose encoder-decoder framework for Tensorflow

•    Python

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A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.The official code used for the Massive Exploration of Neural Machine Translation Architectures paper.

tensorflow translation machine-translation neural-network deeplearning

Seq2Seq-PyTorch - Sequence to Sequence Models with PyTorch

•    Python

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A vanilla sequence to sequence model presented in https://arxiv.org/abs/1409.3215, https://arxiv.org/abs/1406.1078 consits of using a recurrent neural network such as an LSTM (http://dl.acm.org/citation.cfm?id=1246450) or GRU (https://arxiv.org/abs/1412.3555) to encode a sequence of words or characters in a source language into a fixed length vector representation and then deocoding from that representation using another RNN in the target language. An extension of sequence to sequence models that incorporate an attention mechanism was presented in https://arxiv.org/abs/1409.0473 that uses information from the RNN hidden states in the source language at each time step in the deocder RNN. This attention mechanism significantly improves performance on tasks like machine translation. A few variants of the attention model for the task of machine translation have been presented in https://arxiv.org/abs/1508.04025.

pytorch seq2seq deep-learning rnn

seq2seq-signal-prediction - Signal prediction with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

•    Jupyter

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The goal of this project of mine is to bring users to try and experiment with the seq2seq neural network architecture. This is done by solving different simple toy problems about signal prediction. Normally, seq2seq architectures may be used for other more sophisticated purposes than for signal prediction, let's say, language modeling, but this project is an interesting tutorial in order to then get to more complicated stuff. Except the fact I made available an ".py" Python version of this tutorial within the repository, it is more convenient to run the code inside the notebook. The ".py" code exported feels a bit raw as an exportation.

seq2seq tensorflow tensorflow-tutorials