The AmbigNQ dataset is a valuable resource for exploring ambiguity in open-domain question answering. Let me provide you with some details:
The AmbigQA task involves predicting a set of question-answer pairs, where each plausible answer is paired with a disambiguated rewrite of the original question.
Dataset Construction:
The types of ambiguity are diverse and sometimes subtle, often becoming apparent only after examining evidence provided by a very large text corpus.
Dataset Versions:
(1) AmbigQA - University of Washington. https://nlp.cs.washington.edu/ambigqa/. (2) ambig_qa.py · ambig_qa at main - Hugging Face. https://huggingface.co/datasets/ambig_qa/blob/main/ambig_qa.py. (3) dataset_infos.json · ambig_qa at main - Hugging Face. https://huggingface.co/datasets/ambig_qa/blob/main/dataset_infos.json. (4) AmbigQA/AmbigNQ README - GitHub: Let’s build from here. https://github.com/shmsw25/AmbigQA.
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