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---
dataset_info:
- config_name: cleaned
features:
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dtype: string
- name: output
list: string
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num_examples: 86
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dataset_size: 211247625
- config_name: default
features:
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dtype: string
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configs:
- config_name: cleaned
data_files:
- split: train
path: cleaned/train-*
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path: cleaned/validation-*
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path: cleaned/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: odc-by
task_categories:
- text-classification
language:
- en
size_categories:
- 1K<n<10K
tags:
- long-context
- movie
- film
- screenplay
- narrative
---
# MulD: Movie Character Type Classification
this is the Movie Character Types task from [MuLD](https://arxiv.org/abs/2202.07362):
- Task: Classify characters as Hero/Protagonist or Villain/Antagonist
- Data: Movie scripts matched with Wikipedia plot summaries
- Method: Amazon Turk annotation based on plot summaries
- Average length: ~45,000 tokens
- Challenge: Character role understanding from full narrative context
```
@inproceedings{hudson-al-moubayed-2022-muld,
title = "{M}u{LD}: The Multitask Long Document Benchmark",
author = "Hudson, George and
Al Moubayed, Noura",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.392/",
pages = "3675--3685",
abstract = "The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficient techniques for processing much longer inputs. In this paper, we present MuLD: a new long document benchmark consisting of only documents over 10,000 tokens. By modifying existing NLP tasks, we create a diverse benchmark which requires models to successfully model long-term dependencies in the text. We evaluate how existing models perform, and find that our benchmark is much more challenging than their `short document' equivalents. Furthermore, by evaluating both regular and efficient transformers, we show that models with increased context length are better able to solve the tasks presented, suggesting that future improvements in these models are vital for solving similar long document problems. We release the data and code for baselines to encourage further research on efficient NLP models."
}
``` |