MuLD

The Multitask Long Document Benchmark

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.

Repository: https://github.com/ghomasHudson/muld
Paper: https://arxiv.org/abs/2202.07362

Supported Tasks and Leaderboards

The 6 MuLD tasks consist of:

NarrativeQA - A question answering dataset requiring an understanding of the plot of books and films.
HotpotQA - An expanded version of HotpotQA requiring multihop reasoning between multiple wikipedia pages. This expanded version includes the full Wikipedia pages.
** OpenSubtitles**
VLSP (Very Long Scientific Papers)
AO3 Style Change Detection
Character Type Identification

Dataset Structure

The data is presented in a text-to-text format where each instance contains a input string, output string and (optionally) json encoded metadata.

{'input: 'Who was wearing the blue shirt? The beginning...', 'output': ['John'], 'metadata': ''}

Data Fields

input: a string which has a differing structure per task but is presented in a unified format
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.
metadata: Additional metadata which may be helpful for evaluation. In this version, only the OpenSubtitles task contains metadata (for the ContraPro annotations).

Data Splits

Each tasks contains different splits.