ConvLab: Multi-Domain End-to-End Dialog System Platform

@article{Lee2019ConvLabME,
  title={ConvLab: Multi-Domain End-to-End Dialog System Platform},
  author={Sungjin Lee and Qi Zhu and Ryuichi Takanobu and Xiang Li and Yaoqin Zhang and Zheng Zhang and Jinchao Li and Baolin Peng and Xiujun Li and Minlie Huang and Jianfeng Gao},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.08637}
}
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all… Expand
Results of the Multi-Domain Task-Completion Dialog Challenge
TLDR
An overview of the “Multi-domain Task Completion” track (Track 1) at the 8th Dialog System Technology Challenge (DSTC-8) is provided, seeking to develop models that predict user responses when only limited in-domain data is available. Expand
DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue
TLDR
DLGNet-Task is presented, a unified task-oriented dialogue system which employs autoregressive transformer networks such as DLGNet and GPT-2/3 to complete user tasks in multi-turn multi-domain conversations and reduces the level of effort required for developing, deploying, and maintaining intelligent assistants at scale. Expand
SUMBT+LaRL: Effective Multi-Domain End-to-End Neural Task-Oriented Dialog System
  • Hwaran Lee, Seokhwan Jo, Hyungjun Kim, Sangkeun Jung, Tae-Yoon Kim
  • Computer Science
  • IEEE Access
  • 2021
TLDR
This work presents an effective multi-domain end-to-end trainable neural dialog system SUMBT+LaRL that incorporates two previous strong models and facilitates them to be fully differentiable and introduces new reward criteria of reinforcement learning for dialog policy training. Expand
Show Us the Way: Learning to Manage Dialog from Demonstrations
TLDR
At the core of the proposed dialog system is a reinforcement learning algorithm which uses Deep Q-learning from Demonstrations to learn a dialog policy with the help of expert examples, finding that demonstrations are essential to training an accurate dialog policy where both state and action spaces are large. Expand
Overview of the Eighth Dialog System Technology Challenge: DSTC8
TLDR
The task definition, provided datasets, baselines and evaluation set-up for each track, and the results of the submitted systems are summarized to highlight the overall trends of the state-of-the-art technologies for the tasks. Expand
Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion
TLDR
The main motivation of the HEDS system is to comprehensively explore the potential of hierarchical context for sufficiently understanding complex dialogues and achieve first place in automatic evaluation and the second place in human evaluation. Expand
Iride: an Industrial Perspective on Production Grade End to End Dialog System
TLDR
The experience in delivering conversational agents via the development of Iride, a platform able to deploy multi-language task-oriented dialog systems, is described by outlining the requirements and constraints emerging from these on the field experiences. Expand
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2
TLDR
This paper presents an end-to-end neural architecture for dialogue systems that addresses both challenges above and achieves the success rate, language understanding, and response appropriateness in the 8th dialogue systems technology challenge (DSTC8). Expand
SOLOIST: Few-shot Task-Oriented Dialog with A Single Pre-trained Auto-regressive Model
TLDR
A new method SOLOIST is presented, which uses transfer learning to efficiently build task-oriented dialog systems at scale using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. Expand
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weaknessExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 28 REFERENCES
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems
TLDR
This paper empirically shows how Mem2Seq controls each generation step, and how its multi-hop attention mechanism helps in learning correlations between memories. Expand
A Network-based End-to-End Trainable Task-oriented Dialogue System
TLDR
This work introduces a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework that can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain. Expand
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark onExpand
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
TLDR
A novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning is proposed which significantly outperforms state- of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail. Expand
ONENET: Joint domain, intent, slot prediction for spoken language understanding
TLDR
This work presents a unified neural network that jointly performs domain, intent, and slot predictions in spoken language understanding systems and adopts a principled architecture for multitask learning to fold in the state-of-the-art models for each task. Expand
ParlAI: A Dialog Research Software Platform
TLDR
ParlAI (pronounced “par-lay”), an open-source software platform for dialog research implemented in Python, is introduced, to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning. Expand
PyDial: A Multi-domain Statistical Dialogue System Toolkit
TLDR
PyDial is an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules and has been extended to provide multidomain conversational functionality. Expand
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
TLDR
The Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics is introduced, at a size of 10k dialogues, at least one order of magnitude larger than all previous annotated task-oriented corpora. Expand
Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing
TLDR
A novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains, and demonstrates great capability in handling multi-domain dialogues. Expand
The Dialog State Tracking Challenge
TLDR
The dialog state tracking challenge seeks to address this by providing a heterogeneous corpus of 15K human-computer dialogs in a standard format, along with a suite of 11 evaluation metrics, and shows that the suite of performance metrics cluster into 4 natural groups. Expand
...
1
2
3
...