{"id":18832939,"url":"https://github.com/declare-lab/msa-robustness","last_synced_at":"2025-04-14T04:31:35.754Z","repository":{"id":38690613,"uuid":"486797384","full_name":"declare-lab/MSA-Robustness","owner":"declare-lab","description":"NAACL 2022 paper on Analyzing Modality Robustness in Multimodal Sentiment Analysis","archived":false,"fork":false,"pushed_at":"2023-01-21T02:14:24.000Z","size":3601,"stargazers_count":31,"open_issues_count":2,"forks_count":5,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-27T18:21:31.433Z","etag":null,"topics":["multimodal-deep-learning","multimodal-sentiment-analysis","robustness-analysis"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/declare-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-04-29T01:17:07.000Z","updated_at":"2024-08-23T13:55:59.000Z","dependencies_parsed_at":"2022-08-30T20:22:11.936Z","dependency_job_id":null,"html_url":"https://github.com/declare-lab/MSA-Robustness","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/declare-lab%2FMSA-Robustness","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/declare-lab%2FMSA-Robustness/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/declare-lab%2FMSA-Robustness/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/declare-lab%2FMSA-Robustness/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/declare-lab","download_url":"https://codeload.github.com/declare-lab/MSA-Robustness/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248821706,"owners_count":21166941,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["multimodal-deep-learning","multimodal-sentiment-analysis","robustness-analysis"],"created_at":"2024-11-08T01:59:34.376Z","updated_at":"2025-04-14T04:31:35.727Z","avatar_url":"https://github.com/declare-lab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MSA-Robustness\nNAACL 2022 paper on [Analyzing Modality Robustness in Multimodal Sentiment Analysis](https://arxiv.org/pdf/2205.15465.pdf)\n\n# Setup the environment\nConfigure the environment of different models respectively, configure the corresponding environment according to the requirements.txt in the model directory.\n\n# Data Download\n- Install [CMU Multimodal SDK](https://github.com/A2Zadeh/CMU-MultimodalSDK). Ensure, you can perform ```from mmsdk import mmdatasdk```.  \n\n# Running the code\nTake MISA as an example\n\n1. ```cd MISA```\n2. ```cd src```\n3. Set ```word_emb_path``` in ```config.py``` to [glove file](http://nlp.stanford.edu/data/glove.840B.300d.zip).\n4. Set ```sdk_dir``` to the path of CMU-MultimodalSDK.\n3. ```bash run.sh``` When doing robustness training, run the \"TRAIN\" section of run.sh, and when doing diagnostic tests, run the \"TEST\" section of run.sh.\n\n\u0026ensp;\u0026ensp;\u0026ensp;\u0026ensp;```--train_method``` means the robustness training method, one of ```{missing, g_noise, hybird}```, ```missing``` means set to zero noise, ```g_noise``` means set to Gaussian Noise, ```hybird``` means the data of train_changed_pct is set to zero_noise, and the data of train_changed_pct is set to Gaussian_Noise.\n\n\u0026ensp;\u0026ensp;\u0026ensp;\u0026ensp;```--train_changed_modal``` means the modality of change during training, one of ```{language, video, audio}```.\n\n\u0026ensp;\u0026ensp;\u0026ensp;\u0026ensp;```--train_changed_pct``` means the percentage of change during training, can set between ```0~1```.\n\n\u0026ensp;\u0026ensp;\u0026ensp;\u0026ensp;```--test_method``` means the diagnostic tests method, one of ```{missing, g_noise, hybird}```, ```missing``` means set to zero noise, ```g_noise``` means set to Gaussian Noise, ```hybird``` means the data of test_changed_pct is set to zero_noise, and the data of test_changed_pct is set to Gaussian_Noise.\n\n\u0026ensp;\u0026ensp;\u0026ensp;\u0026ensp;```--test_changed_modal``` means the modality of change during testing, one of ```{language, video, audio}```.\n\n\u0026ensp;\u0026ensp;\u0026ensp;\u0026ensp;```--train_changed_pct``` means the percentage of change during testing, can set between ```0~1```.\n\n# Citation\n\n```\n@article{hazarika2022analyzing,\n  title={Analyzing Modality Robustness in Multimodal Sentiment Analysis},\n  author={Hazarika, Devamanyu and Li, Yingting and Cheng, Bo and Zhao, Shuai and Zimmermann, Roger and Poria, Soujanya},\n  publisher={NAACL},\n  year={2022}\n}\n\n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeclare-lab%2Fmsa-robustness","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeclare-lab%2Fmsa-robustness","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeclare-lab%2Fmsa-robustness/lists"}