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--- |
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annotations_creators: |
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- found |
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- crowdsourced |
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language_creators: |
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- found |
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languages: |
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- en-US |
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- de-DE |
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licenses: [] |
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multilinguality: |
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- translation |
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- monolingual |
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pretty_name: The Multitask Long Document Benchmark |
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size_categories: |
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- unknown |
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source_datasets: |
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- original |
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- extended|hotpot_qa |
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- extended|open_subtitles |
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task_categories: |
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- conditional-text-generation |
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- question-answering |
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task_ids: |
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- machine-translation |
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- summarization |
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- abstractive-qa |
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--- |
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# MuLD |
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> The Multitask Long Document Benchmark |
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MuLD (Multitask Long Document Benchmark) is a set of 6 NLP tasks where the inputs consist of at least 10,000 words. The benchmark covers a wide variety of task types including translation, summarization, question answering, and classification. Additionally there is a range of output lengths from a single word classification label all the way up to an output longer than the input text. |
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- **Repository:** https://github.com/ghomasHudson/muld |
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- **Paper:** https://arxiv.org/abs/2202.07362 |
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### Supported Tasks and Leaderboards |
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The 6 MuLD tasks consist of: |
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- **NarrativeQA** - A question answering dataset requiring an understanding of the plot of books and films. |
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- **HotpotQA** - An expanded version of HotpotQA requiring multihop reasoning between multiple wikipedia pages. This expanded version includes the full Wikipedia pages. |
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- **OpenSubtitles** - A translation dataset based on the OpenSubtitles 2018 dataset. The entire subtitles for each tv show is provided, one subtitle per line in both English and German. |
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- **VLSP (Very Long Scientific Papers)** - An expanded version of the Scientific Papers summarization dataset. Instead of removing very long papers (e.g. thesis), we explicitly include them removing any short papers. |
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- **AO3 Style Change Detection** - Consists of documents formed from the work of multiple [Archive of Our Own](ao3.org) authors, where the task is to predict the author for each paragraph. |
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- **Movie Character Types** - Predicting whether a named character is the Hero/Villain given a movie script. |
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### Dataset Structure |
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The data is presented in a text-to-text format where each instance contains a input string, output string and (optionally) json encoded metadata. |
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``` |
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{'input: 'Who was wearing the blue shirt? The beginning...', 'output': ['John'], 'metadata': ''} |
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``` |
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### Data Fields |
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- `input`: a string which has a differing structure per task but is presented in a unified format |
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- `output`: a list of strings where each is a possible answer. Most instances only have a single answer, but some such as narrativeQA and VLSP may have multiple. |
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- `metadata`: Additional metadata which may be helpful for evaluation. In this version, only the OpenSubtitles task contains metadata (for the ContraPro annotations). |
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### Data Splits |
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Each tasks contains different splits depending what was available in the source datasets: |
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| Task Name | Train | Validation | Test | |
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|----------------------------|----|----|-----| |
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| NarrativeQA | ✔️ | ✔️ | ✔️ | |
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| HotpotQA | ✔️ | ✔️ | | |
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| AO3 Style Change Detection | ✔️ | ✔️ | ✔️ | |
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| Movie Character Types | ✔️ | ✔️ | ✔️ | |
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| VLSP | | | ✔️ | |
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| OpenSubtitles | ✔️ | | ✔️ | |
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### Citation Information |
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``` |
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@misc{hudson2022muld, |
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title={MuLD: The Multitask Long Document Benchmark}, |
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author={G Thomas Hudson and Noura Al Moubayed}, |
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year={2022}, |
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eprint={2202.07362}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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Please also cite the papers directly used in this benchmark. |
