Datasets:

ghomasHudson
/

muld

Tasks:

Question Answering

Text Generation

Sub-tasks:

Languages:

Multilinguality:

Size Categories:

Language Creators:

Annotations Creators:

Source Datasets:

extended|hotpot_qa

extended|open_subtitles

ArXiv:

Tags:

conditional-text-generation

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ghomasHudson commited on Feb 16, 2022

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@@ -1,5 +1,5 @@
 # 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.
@@ -9,7 +9,12 @@ MuLD (Multitask Long Document Benchmark) is a set of 6 NLP tasks where the input
 ### 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.

 # 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.
 ### 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.