A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset - The Stanford Natural Language Processing Group
Data Collection Our data was collected using a Wizard-of-Oz scheme inspired by that of Wen et. al. In our scheme, users had two potential modes they could play: Driver and Car Assistant. In the Driver mode, users were presented with a task that listed certain information they were trying to extract from the Car Assistant as well as the dialogue history exchanged between Driver and Car Assistant up
E2E NLG Challenge
Motivation Natural language generation plays a critical role for Conversational Agents as it has a significant impact on a user’s impression of the system. This shared task focuses on recent end-to-end (E2E), data-driven NLG methods, which jointly learn sentence planning and surface realisation from non-aligned data, e.g. (Wen et al., 2015; Mei et al., 2016; Dusek and Jurcicek, 2016; Lampouras and
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is suf
Learning End-to-End Goal-Oriented Dialog
Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End-to-end dialog systems, in which all components are trained from the dialogs themselves, escape this limitation. But the encouraging success recently obtained in chit-chat dialog may not carry over to goal-oriented settings. This paper proposes a
A Network-based End-to-End Trainable Task-oriented Dialogue System
Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-
A Persona-Based Neural Conversation Model
We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speaker-addressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance impr
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GitHub - facebookresearch/ParlAI: A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Latent Variable Dialogue Models and their Diversity
Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
tensortalk.com