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
Data Augmentation Using Keras and Python

Data-Augmentation-Using-Keras-and-Python Data augmentation is the process of increasing the number of training dataset. Keras library offers a simple

Happy N. Monday 3 Feb 15, 2022
Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

MyTT Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python

dev 34 Dec 27, 2022
Using deep learning to predict gene structures of the coding genes in DNA sequences of Arabidopsis thaliana

DeepGeneAnnotator: A tool to annotate the gene in the genome The master thesis of the "Using deep learning to predict gene structures of the coding ge

Ching-Tien Wang 3 Sep 09, 2022
Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at [email protected]

TableParser Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at DS3 Lab 11 Dec 13, 2022

Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
Plaything for Autistic Children (demo for PaddlePaddle/Wechaty/Mixlab project)

星星的孩子 - 一款为孤独症孩子设计的聊天机器人游戏 孤独症儿童是目前常常被忽视的一类群体。他们有着类似性格内向的特征,实际却受着广泛性发育障碍的折磨。 项目背景 这类儿童在与人交往时存在着沟通障碍,其特点表现在: 社交交流差,互动障碍明显 认知能力有限,被动认知 兴趣狭窄,重复刻板,缺乏变化和想象

Tianyi Pan 35 Nov 24, 2022
Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees" Installa

0 Oct 13, 2021
Code examples and benchmarks from the paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective"

Code For the Paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective" Author: Robert Bamler Date: 22 D

4 Nov 02, 2022
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Website | ICCV paper | arXiv | Twitter This repository contains the official i

Ajay Jain 73 Dec 27, 2022
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible

pyrelational is a python active learning library developed by Relation Therapeutics for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximat

Relation Therapeutics 95 Dec 27, 2022
TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision

TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{you2019torchcv, author = {Ansheng You and Xiangtai Li and Zhen Zhu a

Donny You 2.2k Jan 06, 2023
Implementations of paper Controlling Directions Orthogonal to a Classifier

Classifier Orthogonalization Implementations of paper Controlling Directions Orthogonal to a Classifier , ICLR 2022, Yilun Xu, Hao He, Tianxiao Shen,

Yilun Xu 33 Dec 01, 2022
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Python Streaming Anomaly Detection (PySAD) PySAD is an open-source python framework for anomaly detection on streaming multivariate data. Documentatio

Selim Firat Yilmaz 181 Dec 18, 2022
Open-Ended Commonsense Reasoning (NAACL 2021)

Open-Ended Commonsense Reasoning Quick links: [Paper] | [Video] | [Slides] | [Documentation] This is the repository of the paper, Differentiable Open-

(Bill) Yuchen Lin 31 Oct 19, 2022
Distributionally robust neural networks for group shifts

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization This code implements the g

151 Dec 25, 2022
The implementation for the SportsCap (IJCV 2021)

SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos ProjectPage | Paper | Video | Dataset (Part01

Chen Xin 79 Dec 16, 2022
Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge Introduction SentiLARE is a sentiment-aware pre-trained language

74 Dec 30, 2022
📝 Wrapper library for text generation / language models at char and word level with RNN in TensorFlow

tensorlm Generate Shakespeare poems with 4 lines of code. Installation tensorlm is written in / for Python 3.4+ and TensorFlow 1.1+ pip3 install tenso

Kilian Batzner 63 May 22, 2021
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation We propose a novel approach to translate unpaired contrast computed

Nicolae Catalin Ristea 13 Jan 02, 2023
Public Models considered for emotion estimation from EEG

Emotion-EEG Set of models for emotion estimation from EEG. Composed by the combination of two deep-learing models learning together (RNN and CNN) with

Victor Delvigne 21 Dec 23, 2022