Welcome to the Situated Interactive Multimodal Conversations (SIMMC) Track for DSTC9 2020.
The SIMMC challenge aims to lay the foundations for the real-world assistant agents that can handle multimodal inputs, and perform multimodal actions. We thus focus on task-oriented dialogs that encompass a situated multimodal user context in the form of a co-observed image or virtual reality (VR) environment. The context is dynamically updated on each turn based on the user input and the assistant action. Our challenge focuses on our SIMMC datasets, both of which are shopping domains: (a) furniture (grounded in a shared virtual environment) and, (b) fashion (grounded in an evolving set of images).
Organizers: Ahmad Beirami, Eunjoon Cho, Paul A. Crook, Ankita De, Alborz Geramifard, Satwik Kottur, Seungwhan Moon, Shivani Poddar, Rajen Subba
- [Apr 15, 2021] Released screenshots for SIMMC-Furniture (part 0, part 1, part 2). Also released improved API calls with newer heuristics as SIMMC v1.2 (PR).
- [Dec 29, 2020] Fixed the errors in text spans for both SIMMC-Furniture and SIMMC-Fashion, released new JSON files as SIMMC v1.1 (PR).
- [Sept 28, 2020] Test-Std data released, End of Challenge Phase 1.
- [July 8, 2020] Evaluation scripts and code to train baselines for Sub-Task #1, Sub-Task #2 released.
- [June 22, 2020] Challenge announcement. Training / development datasets (SIMMC v1.0) are released.
Note: DSTC9 SIMMC Challenge was conducted on SIMMC v1.0. Thus all the results and baseline performances are on SIMMC v1.0.
- Task Description Paper
- Challenge Registration
- Data Formats
- Baseline Details: MM Action Prediction, MM Response Generation, MM-DST
- Challenge Instructions
- Submission Instructions
| Date | Milestone |
|---|---|
| June 22, 2020 | Training & development data released |
| Sept 28, 2020 | Test-Std data released, End of Challenge Phase 1 |
| Oct 5, 2020 | Entry submission deadline, End of Challenge Phase 2 |
| Oct 12, 2020 | Final results announced |
We present three sub-tasks primarily aimed at replicating human-assistant actions in order to enable rich and interactive shopping scenarios.
| Sub-Task #1 | Multimodal Action Prediction |
|---|---|
| Goal | To predict the correct Assistant API action(s) (classification) |
| Input | Current user utterance, Dialog context, Multimodal context |
| Output | Structural API (action & arguments) |
| Metrics | Action Accuracy, Attribute Accuracy, Action Perplexity |
| Sub-Task #2 | 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, R@1, R@5, R@10, Mean Rank |
| 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 |
Please check the task input file for a full description of inputs for each subtask.
For the DSTC9 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 28), 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 5.
We will announce the final results and the winners on Oct 12.
- Fill out this form to register at DSTC9. Check “Track 4: Visually Grounded Dialog Track” along with other tracks you are participating in.
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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.
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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/simmc.git
- Submit your model prediction results on the
devtestset, following the submission instructions. - We will release the
test-stdset (with ground-truth labels hidden) on Sept 28.
- Submit your model prediction results on the
test-stdset, following the submission instructions. - We will evaluate the participants’ model predictions using the same evaluation script for Phase 1, and announce the results.
Please contact simmc@fb.com, or leave comments in the Github repository.
If you want to get the latest updates about DSTC9, join the DSTC mailing list.
If you want to publish experimental results with our datasets or use the baseline models, please cite the following articles:
@article{moon2020situated,
title={Situated and Interactive Multimodal Conversations},
author={Moon, Seungwhan and Kottur, Satwik and Crook, Paul A and De, Ankita and Poddar, Shivani and Levin, Theodore and Whitney, David and Difranco, Daniel and Beirami, Ahmad and Cho, Eunjoon and Subba, Rajen and Geramifard, Alborz},
journal={arXiv preprint arXiv:2006.01460},
year={2020}
}
@article{crook2019simmc,
title={SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform},
author={Crook, Paul A and Poddar, Shivani and De, Ankita and Shafi, Semir and Whitney, David and Geramifard, Alborz and Subba, Rajen},
journal={arXiv preprint arXiv:1911.02690},
year={2019}
}
NOTE: The paper above describes in detail the datasets, the NLU/NLG/Coref annotations, 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.
SIMMC is released under CC-BY-NC-SA-4.0, see LICENSE for details.