{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/collecting-interactive-multi-modal-datasets","title":"Collecting Interactive Multi-modal Datasets for Grounded Language Understanding","arxiv_id":"2211.06552","date":"2022-11-12","proceeding":null,"authors":["Shrestha Mohanty","Negar Arabzadeh","Milagro Teruel","Yuxuan Sun","Artem Zholus","Alexey Skrynnik","Mikhail Burtsev","Kavya Srinet","Aleksandr Panov","Arthur Szlam","Marc-Alexandre Côté","Julia Kiseleva"],"abstract":"Human intelligence can remarkably adapt quickly to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research which can enable similar capabilities in machines, we made the following contributions (1) formalized the collaborative embodied agent using natural language task; (2) developed a tool for extensive and scalable data collection; and (3) collected the first dataset for interactive grounded language understanding.","url_abs":"https://arxiv.org/abs/2211.06552v3","url_pdf":"https://arxiv.org/pdf/2211.06552v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"collecting-interactive-multi-modal-datasets","repo_url":"https://github.com/iglu-contest/iglu-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"collecting-interactive-multi-modal-datasets","repo_url":"https://github.com/iglu-contest/nlp-baselines-2022","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[{"slug":"iglu","name":"IGLU","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.06552","atlas_url":"https://app.syntology.ai/?focus=2211.06552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.06552"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/iglu-contest/iglu-dataset","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/iglu-contest/nlp-baselines-2022","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":4},"by_repo_kind":{"official":{"samples":4,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"afea8960735ad203","entry":"break_str_to_lines","repo":"iglu-contest/iglu-dataset","repo_kind":"official","path":"utils/grid_visualization.py","file_url":"https://github.com/iglu-contest/iglu-dataset/blob/HEAD/utils/grid_visualization.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"afea8960735ad203"}},{"code_sha256_prefix":"7a40668326b78875","entry":"get_logger","repo":"iglu-contest/iglu-dataset","repo_kind":"official","path":"mturk_scripts/common/logger.py","file_url":"https://github.com/iglu-contest/iglu-dataset/blob/HEAD/mturk_scripts/common/logger.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7a40668326b78875"}},{"code_sha256_prefix":"bff77c10dec423a0","entry":"plot_grid","repo":"iglu-contest/iglu-dataset","repo_kind":"official","path":"utils/grid_visualization.py","file_url":"https://github.com/iglu-contest/iglu-dataset/blob/HEAD/utils/grid_visualization.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bff77c10dec423a0"}},{"code_sha256_prefix":"cff24b9341d6e534","entry":"read_config","repo":"iglu-contest/iglu-dataset","repo_kind":"official","path":"mturk_scripts/common/utils.py","file_url":"https://github.com/iglu-contest/iglu-dataset/blob/HEAD/mturk_scripts/common/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cff24b9341d6e534"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}