{"url":"/dataset/tom-in-amc","name":"ToM-in-AMC","full_name":null,"description_markdown":"**ToM-in-AMC** is a novel NLP benchmark, Short for Theory-of-Mind meta-learning Assessment with Movie Characters. The benchmark consists of 1,000 parsed movie scripts for this purpose, each corresponding to a few-shot character understanding task.\r\n\r\nSource: [Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind](https://arxiv.org/pdf/2211.04684.pdf)\r\n\r\nImage Source: [https://arxiv.org/pdf/2211.04684v1.pdf](https://arxiv.org/pdf/2211.04684v1.pdf)","description_withheld":null,"homepage":"https://shunchizhang.github.io/tom-in-amc","introduced_date":"2022-11-09","introduced_date_note":null,"introduced_by":{"paper":"/paper/few-shot-character-understanding-in-movies-as","title":"Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind","first_author":"Mo Yu","url":null},"license":{"name":"Apache-2.0 license","url":"https://github.com/Gorov/tom_in_amc/blob/main/LICENSE"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Meta-Learning","url":"/task/meta-learning","datasets_with_task":"/datasets/task/meta-learning"},{"name":"Few-Shot Learning","url":"/task/few-shot-learning","datasets_with_task":"/datasets/task/few-shot-learning"}],"languages":[],"variants":["ToM-in-AMC"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}