|
# MuLD |
|
> 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: |
|
|
|
|
|
### 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. |