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
Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts". Coming soon!

ToxiChat Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts". Install depen

Ashutosh Baheti 11 Jan 01, 2023
Download and preprocess popular sequential recommendation datasets

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

125 Dec 06, 2022
MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet.

Lightweight-Detection-and-KD MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet. This repo also includes detection knowledge di

Egqawkq 12 Jan 05, 2023
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

RINDNet RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Lin

Mengyang Pu 75 Dec 15, 2022
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Piggyback: https://arxiv.org/abs/1801.06519 Pretrained masks and backbones are available here: https://uofi.box.com/s/c5kixsvtrghu9yj51yb1oe853ltdfz4q

Arun Mallya 165 Nov 22, 2022
A collection of implementations of deep domain adaptation algorithms

Deep Transfer Learning on PyTorch This is a PyTorch library for deep transfer learning. We divide the code into two aspects: Single-source Unsupervise

Yongchun Zhu 647 Jan 03, 2023
Simple-System-Convert--C--F - Simple System Convert With Python

Simple-System-Convert--C--F REQUIREMENTS Python version : 3 HOW TO USE Run the c

Jonathan Santos 2 Feb 16, 2022
A curated list and survey of awesome Vision Transformers.

English | 简体中文 A curated list and survey of awesome Vision Transformers. You can use mind mapping software to open the mind mapping source file. You c

OpenMMLab 281 Dec 21, 2022
Pytorch implementation of the paper Time-series Generative Adversarial Networks

TimeGAN-pytorch Pytorch implementation of the paper Time-series Generative Adversarial Networks presented at NeurIPS'19. Jinsung Yoon, Daniel Jarrett

Zhiwei ZHANG 21 Nov 24, 2022
A Machine Teaching Framework for Scalable Recognition

MEMORABLE This repository contains the source code accompanying our ICCV 2021 paper. A Machine Teaching Framework for Scalable Recognition Pei Wang, N

2 Dec 08, 2021
CAR-API: Cityscapes Attributes Recognition API

CAR-API: Cityscapes Attributes Recognition API This is the official api to download and fetch attributes annotations for Cityscapes Dataset. Content I

Kareem Metwaly 5 Dec 22, 2022
An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicity.

Fast Face Classification (F²C) This is the code of our paper An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicit

33 Jun 27, 2021
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

CM-NAS Official Pytorch code of paper CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification in ICCV2021. Vis

JDAI-CV 40 Nov 25, 2022
This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset.

FACT This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset. To cite, please use:

105 Dec 17, 2022
Official implementation of ACMMM'20 paper 'Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework'

Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework Official code for paper, Self-supervised Video Representation Le

Li Tao 103 Dec 21, 2022
A simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

This is a simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

crispengari 3 Jan 08, 2022
Contrastive Learning Inverts the Data Generating Process

Official code to reproduce the results and data presented in the paper Contrastive Learning Inverts the Data Generating Process.

71 Nov 25, 2022
EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures

SCICAP: Scientific Figures Dataset This is the Github repo of the EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures (Hsu

Edward 26 Nov 21, 2022
Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The original code is written in keras.

CasRel-pytorch-reimplement Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The o

longlongman 170 Dec 01, 2022
Fastquant - Backtest and optimize your trading strategies with only 3 lines of code!

fastquant 🤓 Bringing backtesting to the mainstream fastquant allows you to easily backtest investment strategies with as few as 3 lines of python cod

Lorenzo Ampil 1k Dec 29, 2022