Code for SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations

Related tags

Deep Learningsimmc2
Overview

The Second Situated Interactive MultiModal Conversations (SIMMC 2.0) Challenge 2021

Welcome to the Second Situated Interactive Multimodal Conversations (SIMMC 2.0) Track for DSTC10 2021.

The SIMMC challenge aims to lay the foundations for the real-world assistant agents that can handle multimodal inputs, and perform multimodal actions. Similar to the First SIMMC challenge (as part of DSTC9), we focus on the task-oriented dialogs that encompass a situated multimodal user context in the form of a co-observed & immersive virtual reality (VR) environment. The conversational context is dynamically updated on each turn based on the user actions (e.g. via verbal interactions, navigation within the scene). For this challenge, we release a new Immersive SIMMC 2.0 dataset in the shopping domains: furniture and fashion.

Organizers: Seungwhan Moon, Satwik Kottur, Paul A. Crook, Ahmad Beirami, Babak Damavandi, Alborz Geramifard

Example from SIMMC

Example from SIMMC-Furniture Dataset

Latest News

  • [June 14, 2021] Challenge announcement. Training / development datasets (SIMMC v2.0) are released.

Important Links

Timeline

Date Milestone
June 14, 2021 Training & development data released
Sept 24, 2021 Test-Std data released, End of Challenge Phase 1
Oct 1, 2021 Entry submission deadline, End of Challenge Phase 2
Oct 8, 2021 Final results announced

Track Description

Tasks and Metrics

We present four sub-tasks primarily aimed at replicating human-assistant actions in order to enable rich and interactive shopping scenarios.

Sub-Task #1 Multimodal Disambiguation
Goal To classify if the assistant should disambiguate in the next turn
Input Current user utterance, Dialog context, Multimodal context
Output Binary label
Metrics Binary classification accuracy
Sub-Task #2 Multimodal Coreference Resolution
Goal To resolve referent objects to thier canonical ID(s) as defined by the catalog.
Input Current user utterance with objection mentions, Dialog context, Multimodal context
Output Canonical object IDs
Metrics Coref F1 / Precision / Recall
Sub-Task #3 Multimodal Dialog State Tracking (MM-DST)
Goal To track user belief states across multiple turns
Input Current user utterance, Dialogue context, Multimodal context
Output Belief state for current user utterance
Metrics Slot F1, Intent F1
Sub-Task #4 Multimodal Dialog Response Generation & Retrieval
Goal To generate Assistant responses or retrieve from a candidate pool
Input Current user utterance, Dialog context, Multimodal context, (Ground-truth API Calls)
Output Assistant response utterance
Metrics Generation: BLEU-4, Retrieval: MRR, [email protected], [email protected], [email protected], Mean Rank

Please check the task input file for a full description of inputs for each subtask.

Evaluation

For the DSTC10 SIMMC Track, we will do a two phase evaluation as follows.

Challenge Period 1: Participants will evaluate the model performance on the provided devtest set. At the end of Challenge Period 1 (Sept 24), we ask participants to submit their model prediction results and a link to their code repository.

Challenge Period 2: A test-std set will be released on Sept 28 for the participants who submitted the results for the Challenge Period 1. We ask participants to submit their model predictions on the test-std set by Oct 1. We will announce the final results and the winners on Oct 8.

Challenge Instructions

(1) Challenge Registration

  • Fill out this form to register at DSTC10. Check “Track 3: SIMMC 2.0: Situated Interactive Multimodal Conversational AI” along with other tracks you are participating in.

(2) Download Datasets and Code

  • Irrespective of participation in the challenge, we'd like to encourge those interested in this dataset to complete this optional survey. This will also help us communicate any future updates on the codebase, the datasets, and the challenge track.

  • Git clone our repository to download the datasets and the code. You may use the provided baselines as a starting point to develop your models.

$ git lfs install
$ git clone https://github.com/facebookresearch/simmc2.git

(3) Reporting Results for Challenge Phase 1

  • Submit your model prediction results on the devtest set, following the submission instructions.
  • We will release the test-std set (with ground-truth labels hidden) on Sept 24.

(4) Reporting Results for Challenge Phase 2

  • Submit your model prediction results on the test-std set, following the submission instructions.
  • We will evaluate the participants’ model predictions using the same evaluation script for Phase 1, and announce the results.

Contact

Questions related to SIMMC Track, Data, and Baselines

Please contact [email protected], or leave comments in the Github repository.

DSTC Mailing List

If you want to get the latest updates about DSTC10, join the DSTC mailing list.

Citations

If you want to publish experimental results with our datasets or use the baseline models, please cite the following articles:

@article{kottur2021simmc,
  title={SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations},
  author={Kottur, Satwik and Moon, Seungwhan and Geramifard, Alborz and Damavandi, Babak},
  journal={arXiv preprint arXiv:2104.08667},
  year={2021}
}

NOTE: The paper above describes in detail the datasets, the collection process, and some of the baselines we provide in this challenge. The paper reports the results from an earlier version of the dataset and with different train-dev-test splits, hence the baseline performances on the challenge resources will be slightly different.

License

SIMMC 2.0 is released under CC-BY-NC-SA-4.0, see LICENSE for details.

Owner
Facebook Research
Facebook Research
TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

Microsoft 1.3k Dec 30, 2022
A minimal TPU compatible Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

NeRF Minimal Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Result of Tiny-NeRF RGB Depth

Soumik Rakshit 11 Jul 24, 2022
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling

Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling Code for the paper: Greg Ver Steeg and Aram Galstyan. "Hamiltonian Dynamics with N

Greg Ver Steeg 25 Mar 14, 2022
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

Object Pose Estimation Demo This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. You’ll gain

Unity Technologies 187 Dec 24, 2022
Video Swin Transformer - PyTorch

Video-Swin-Transformer-Pytorch This repo is a simple usage of the official implementation "Video Swin Transformer". Introduction Video Swin Transforme

Haofan Wang 116 Dec 20, 2022
An NVDA add-on to split screen reader and audio from other programs to different sound channels

An NVDA add-on to split screen reader and audio from other programs to different sound channels (add-on idea credit: Tony Malykh)

Joseph Lee 7 Dec 25, 2022
Synthesize photos from PhotoDNA using machine learning 🌱

Ribosome Synthesize photos from PhotoDNA. See the blog post for more information. Installation Dependencies You can install Python dependencies using

Anish Athalye 112 Nov 23, 2022
Style transfer, deep learning, feature transform

FastPhotoStyle License Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons

NVIDIA Corporation 10.9k Jan 02, 2023
Convolutional 2D Knowledge Graph Embeddings resources

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

Tim Dettmers 586 Dec 24, 2022
The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."

FFA-IR The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark." The framework is inheri

Mingjie 28 Dec 16, 2022
Transport Mode detection - can detect the mode of transport with the help of features such as acceeration,jerk etc

title emoji colorFrom colorTo sdk app_file pinned Transport_Mode_Detector 🚀 purple yellow gradio app.py false Configuration title: string Display tit

Nishant Rajadhyaksha 3 Jan 16, 2022
Barlow Twins and HSIC

Barlow Twins and HSIC Unofficial Pytorch implementation for Barlow Twins and HSIC_SSL on small datasets (CIFAR10, STL10, and Tiny ImageNet). Correspon

Yao-Hung Hubert Tsai 49 Nov 24, 2022
This is a vision-based 3d model manipulation and control UI

Manipulation of 3D Models Using Hand Gesture This program allows user to manipulation 3D models (.obj format) with their hands. The project support bo

Cortic Technology Corp. 43 Oct 23, 2022
Learning to Map Large-scale Sparse Graphs on Memristive Crossbar

Release of AutoGMap:Learning to Map Large-scale Sparse Graphs on Memristive Crossbar For reproduction of our searched model, the Ubuntu OS is recommen

2 Aug 23, 2022
Automatically creates genre collections for your Plex media

Plex Auto Genres Plex Auto Genres is a simple script that will add genre collection tags to your media making it much easier to search for genre speci

Shane Israel 63 Dec 31, 2022
LRBoost is a scikit-learn compatible approach to performing linear residual based stacking/boosting.

LRBoost is a sckit-learn compatible package for linear residual boosting. LRBoost combines a linear estimator and a non-linear estimator to leverage t

Andrew Patton 5 Nov 23, 2022
GEA - Code for Guided Evolution for Neural Architecture Search

Efficient Guided Evolution for Neural Architecture Search Usage Create a conda e

6 Jan 03, 2023
Algorithmic trading using machine learning.

Algorithmic Trading This machine learning algorithm was built using Python 3 and scikit-learn with a Decision Tree Classifier. The program gathers sto

Sourav Biswas 101 Nov 10, 2022
Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring

Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring (to appear at AAAI 2022) We propose a machine-learning-bas

YunzhuangS 2 May 02, 2022
offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

PeterPu 76 Dec 08, 2022