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
Creating multimodal multitask models

Fusion Brain Challenge The English version of the document can be found here. Обновления 01.11 Мы выкладываем пример данных, аналогичных private test

Sber AI 43 Nov 28, 2022
Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

7 Jun 22, 2022
A Broader Picture of Random-walk Based Graph Embedding

Random-walk Embedding Framework This repository is a reference implementation of the random-walk embedding framework as described in the paper: A Broa

Zexi Huang 23 Dec 13, 2022
Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

Online Multi-Granularity Distillation for GAN Compression (ICCV2021) This repository contains the pytorch codes and trained models described in the IC

Bytedance Inc. 299 Dec 16, 2022
Sign Language Translation with Transformers (COLING'2020, ECCV'20 SLRTP Workshop)

transformer-slt This repository gathers data and code supporting the experiments in the paper Better Sign Language Translation with STMC-Transformer.

Kayo Yin 107 Dec 27, 2022
A parametric soroban written with CADQuery.

A parametric soroban written in CADQuery The purpose of this project is to demonstrate how "code CAD" can be intuitive to learn. See soroban.py for a

Lee 4 Aug 13, 2022
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

NuPIC Numenta Platform for Intelligent Computing The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implem

Numenta 6.3k Dec 30, 2022
IGCN : Image-to-graph convolutional network

IGCN : Image-to-graph convolutional network IGCN is a learning framework for 2D/3D deformable model registration and alignment, and shape reconstructi

Megumi Nakao 7 Oct 27, 2022
🗣️ Microsoft Edge TTS for Home Assistant, no need for app_key

Microsoft Edge TTS for Home Assistant This component is based on the TTS service of Microsoft Edge browser, no need to apply for app_key. Install Down

152 Dec 31, 2022
Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

[AAAI22] Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification We point out the overlooked unbiasedness in long-tailed clas

PatatiPatata 28 Oct 18, 2022
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

AnimeGAN A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. Randomly Generated Images The images are

Jie Lei 雷杰 1.2k Jan 03, 2023
Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight

Implicit Constraint Q-Learning This is a pytorch implementation of ICQ on Datasets for Deep Data-Driven Reinforcement Learning (D4RL) and ICQ-MA on SM

42 Dec 23, 2022
The code uses SegFormer for Semantic Segmentation on Drone Dataset.

SegFormer_Segmentation The code uses SegFormer for Semantic Segmentation on Drone Dataset. The details for the SegFormer can be obtained from the foll

Dr. Sander Ali Khowaja 1 May 08, 2022
Ranking Models in Unlabeled New Environments (iccv21)

Ranking Models in Unlabeled New Environments Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch 1.7.0 + torchivision 0.8.1

14 Dec 17, 2021
Pre-trained Deep Learning models and demos (high quality and extremely fast)

OpenVINO™ Toolkit - Open Model Zoo repository This repository includes optimized deep learning models and a set of demos to expedite development of hi

OpenVINO Toolkit 3.4k Dec 31, 2022
Knowledge Management for Humans using Machine Learning & Tags

HyperTag HyperTag helps humans intuitively express how they think about their files using tags and machine learning.

Ravn Tech, Inc. 165 Nov 04, 2022
TabNet for fastai

TabNet for fastai This is an adaptation of TabNet (Attention-based network for tabular data) for fastai (=2.0) library. The original paper https://ar

Mikhail Grankin 116 Oct 21, 2022
这是一个mobilenet-yolov4-lite的库,把yolov4主干网络修改成了mobilenet,修改了Panet的卷积组成,使参数量大幅度缩小。

YOLOV4:You Only Look Once目标检测模型-修改mobilenet系列主干网络-在Keras当中的实现 2021年2月8日更新: 加入letterbox_image的选项,关闭letterbox_image后网络的map一般可以得到提升。

Bubbliiiing 65 Dec 01, 2022
Official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection

Adaptive Class Suppression Loss for Long-Tail Object Detection This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppressio

CASIA-IVA-Lab 67 Dec 04, 2022
Hack Camera, Microphone, Location, Clipboard With Just a Link. Also, Get Many Details About Victim's Device. And So On...

An Automated Tool to Hack Victim's Camera, Microphone, Location, Clipboard. Has 2 Extra Features. Version 1.1 Update Fixed Some Major Bugs Data Saving

ToxicNoob 36 Jan 07, 2023