DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations

Overview

License

DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations

This repository contains the data, scripts and baseline codes for DSTC10 Track 2.

This challenge track aims to benchmark the robustness of the conversational models against the gaps between written and spoken conversations. Specifically, it includes two target tasks: 1) multi-domain dialogue state tracking and 2) task-oriented conversational modeling with unstructured knowledge access. For both tasks, participants will develop models using any existing public data and submit the model outputs on the unlabeled test data set with the ASR outputs.

Organizers: Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Behnam Hedayatnia, Karthik Gopalakrishnan, Dilek Hakkani-Tur

Important Links

Timeline

  • Validation data released: Jun 14, 2021
  • Test data released: Sep 13, 2021
  • Entry submission deadline: Sep 21, 2021
  • Objective evaluation completed: Sep 28, 2021
  • Human evaluation completed: Oct 8, 2021

Rules

  • Participation is welcome from any team (academic, corporate, non profit, government).
  • Each team can participate in either or both sub-tracks by submitting up to 5 entries for each track.
  • The identity of participants will NOT be published or made public. In written results, teams will be identified as team IDs (e.g. team1, team2, etc). The organizers will verbally indicate the identities of all teams at the workshop chosen for communicating results.
  • Participants may identify their own team label (e.g. team5), in publications or presentations, if they desire, but may not identify the identities of other teams.
  • Participants are allowed to use any external datasets, resources or pre-trained models which are publicly available.
  • Participants are NOT allowed to do any manual examination or modification of the test data.
  • All the submitted system outputs with the evaluation results will be released to public after the evaluation period.

Contact

Join the DSTC mailing list to get the latest updates about DSTC10

For specific enquiries about DSTC10 Track2

Please feel free to contact: seokhwk (at) amazon (dot) com

Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference

RawVSR This repo contains the official codes for our paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference Xiaoh

Xiaohong Liu 23 Oct 08, 2022
dataset for ECCV 2020 "Motion Capture from Internet Videos"

Motion Capture from Internet Videos Motion Capture from Internet Videos Junting Dong*, Qing Shuai*, Yuanqing Zhang, Xian Liu, Xiaowei Zhou, Hujun Bao

ZJU3DV 98 Dec 07, 2022
Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in "Feature Learning in Infinite-width Neural Networks" This repo contains code to replicate our experiments (Word2Vec, MAML) in

Edward Hu 37 Dec 14, 2022
PyTorch code for the ICCV'21 paper: "Always Be Dreaming: A New Approach for Class-Incremental Learning"

Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning PyTorch code for the ICCV 2021 paper: Always Be Dreaming: A New Approach f

49 Dec 21, 2022
iris - Open Source Photos Platform Powered by PyTorch

Open Source Photos Platform Powered by PyTorch. Submission for PyTorch Annual Hackathon 2021.

Omkar Prabhu 137 Sep 10, 2022
Fuzzer for Linux Kernel Drivers

difuze: Fuzzer for Linux Kernel Drivers This repo contains all the sources (including setup scripts), you need to get difuze up and running. Tested on

seclab 344 Dec 27, 2022
Python wrapper of LSODA (solving ODEs) which can be called from within numba functions.

numbalsoda numbalsoda is a python wrapper to the LSODA method in ODEPACK, which is for solving ordinary differential equation initial value problems.

Nick Wogan 52 Jan 09, 2023
This codebase proposes modular light python and pytorch implementations of several LiDAR Odometry methods

pyLiDAR-SLAM This codebase proposes modular light python and pytorch implementations of several LiDAR Odometry methods, which can easily be evaluated

Kitware, Inc. 208 Dec 16, 2022
Artificial Neural network regression model to predict the energy output in a combined cycle power plant.

Energy_Output_Predictor Artificial Neural network regression model to predict the energy output in a combined cycle power plant. Abstract Energy outpu

1 Feb 11, 2022
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Segmentation Transformer Implementation of Segmentation Transformer in PyTorch, a new model to achieve SOTA in semantic segmentation while using trans

Abhay Gupta 161 Dec 08, 2022
PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identification in Symbolic Scores.

Symbolic Melody Identification This repository is an unofficial PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identifica

Sophia Y. Chou 3 Feb 21, 2022
Generate images from texts. In Russian. In PaddlePaddle

ruDALL-E PaddlePaddle ruDALL-E in PaddlePaddle. Install: pip install rudalle_paddle==0.0.1rc1 Run with free v100 on AI Studio. Original Pytorch versi

AgentMaker 20 Oct 18, 2022
Info and sample codes for "NTU RGB+D Action Recognition Dataset"

"NTU RGB+D" Action Recognition Dataset "NTU RGB+D 120" Action Recognition Dataset "NTU RGB+D" is a large-scale dataset for human action recognition. I

Amir Shahroudy 578 Dec 30, 2022
Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr

Google 729 Jan 02, 2023
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
Pytorch Implementation of "Desigining Network Design Spaces", Radosavovic et al. CVPR 2020.

RegNet Pytorch Implementation of "Desigining Network Design Spaces", Radosavovic et al. CVPR 2020. Paper | Official Implementation RegNet offer a very

Vishal R 2 Feb 11, 2022
Computational inteligence project on faces in the wild dataset

Table of Contents The general idea How these scripts work? Loading data Needed modules and global variables Parsing the arrays in dataset Extracting a

tooraj taraz 4 Oct 21, 2022
Deep Residual Networks with 1K Layers

Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc

Kaiming He 856 Jan 06, 2023
Object recognition using Azure Custom Vision AI and Azure Functions

Step by Step on how to create an object recognition model using Custom Vision, export the model and run the model in an Azure Function

El Bruno 11 Jul 08, 2022
3D Generative Adversarial Network

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling This repository contains pre-trained models and sampling

Chengkai Zhang 791 Dec 20, 2022