Displaying 1 to 18 from 18 results

cakechat - CakeChat: Emotional Generative Dialog System

  •    Python

It is written in Theano and Lasagne. It uses end-to-end trained embeddings of 5 different emotions to generate responses conditioned by a given emotion. The code is flexible and allows to condition a response by an arbitrary categorical variable defined for some samples in the training data. With CakeChat you can, for example, train your own persona-based neural conversational model[5] or create an emotional chatting machine without external memory[4].

plato-research-dialogue-system - This is the Plato Research Dialogue System, a flexible platform for developing conversational AI agents

  •    Python

The Plato Research Dialogue System is a flexible framework that can be used to create, train, and evaluate conversational AI agents in various environments. It supports interactions through speech, text, or dialogue acts and each conversational agent can interact with data, human users, or other conversational agents (in a multi-agent setting). Every component of every agent can be trained independently online or offline and Plato provides an easy way of wrapping around virtually any existing model, as long as Plato's interface is adhered to. Plato wrote several dialogues between characters who argue on a topic by asking questions. Many of these dialogues feature Socrates including Socrates' trial. (Socrates was acquitted in a new trial held in Athens, Greece on May 25th 2012).

dstc8-schema-guided-dialogue - The Schema-Guided Dialogue Dataset


The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, user simulation learning, among other tasks in large-scale virtual assistants. Besides these, the dataset has unseen domains and services in the evaluation set to quantify the performance in zero-shot or few shot settings. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of this dataset.

NeuralDialog-CVAE - Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation

  •    OpenEdge

We provide a TensorFlow implementation of the CVAE-based dialog model described in Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders, published as a long paper in ACL 2017. See the paper for more details. The outputs will be printed to stdout and generated responses will be saved at test.txt in the test_path.

NeuralDialog-LAED - PyTorch implementation for Interpretable Dialog Generation ACL 2018, It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU

  •    OpenEdge

Codebase for Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation, published as a long paper in ACL 2018. You can find my presentation slides here. The first two scripts are sentence models (DI-VAE/DI-VST) that learn discrete sentence representations from either auto-encoding or context-predicting.

KB-InfoBot - A dialogue bot for information access

  •    Python

This repository contains all the code and data accompanying the paper Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access. IMPORTANT: Download the data and pretrained models from here, unpack the tar and place it at the root of the repository.

ConvAI-baseline - ConvAI baseline solution

  •    Python

Python packages will be installed by setup.sh script. Setup will download docker images, models and data files, so you have no need to download any of that by yourself.

glad - Global-Locally Self-Attentive Dialogue State Tracker

  •    Python

The first time you preprocess the data, we will download word embeddings and character embeddings and put them into a SQLite database, which will be slow. Subsequent runs will be much faster. The raw data will be stored in data/woz/raw of the container. The annotation results will be stored in data/woz/ann of the container.

GoNorth - GoNorth is a story and content planning tool for RPGs and other open world games.

  •    Javascript

GoNorth is a web application used for planning the story and world of RPGs or other open world games. GoNorth is cross-plattform ready, provides multilanguage support and is designed as a responsive layout. Please refer to the wiki for deployment details and the official documentation on how to host and deploy an ASP.NET Core application.

convai-bot-1337 - Skill-based Conversational Agent for NIPS Conversational Intelligence Challenge 2017

  •    Python

Skill-based Conversational Agent that took 1st place at 2017 NIPS Conversational Intelligence Challenge (http://convai.io). We still update our Conversational Agent and the latest version could be found in master branch.

alex - Alex Dialogue Systems Framework

  •    Python

The Alex Dialogue Systems Framework is named after the famous parrot Alex. This framework is being developed by the dialogue systems group at UFAL - http://ufal.mff.cuni.cz/ - the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. The purpose of this work is to facilitate research into and development of spoken dialogue systems.

tgen - Statistical NLG for spoken dialogue systems

  •    Python

Both algoritms can be trained from pairs of source meaning representations (dialogue acts) and target sentences. The newer seq2seq approach is preferrable: it yields higher performance in terms of both speed and quality. Both algorithms support generating sentence plans (deep syntax trees), which are subsequently converted to text using the existing the surface realizer from Treex NLP toolkit. The seq2seq algorithm also supports direct string generation.


  •    Python

ADEM is an automatic evaluation model for the quality of dialogue, aiming to capture the semantic similarity beyond word overlapping metrics (e.g BLEU, ROUGH, METOER) which correlating badly to human judgement, and calculate its score using extra information the context of conversation besides the reference response and model response. where M, N are learned parameters initialized with identity, $\alpha$, $\beta$ are scalar constants intialized in the range [0, 5]. The first and second term of the score function can be interpreted as the similarity of model response to context and reference response ,respectively in a linear transformation.

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