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--- |
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license: other |
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--- |
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# Natural Instructions v2 Coreference Tasks |
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- Project: https://github.com/allenai/natural-instructions |
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- Data source: [DataProvenanceInitiative/niv2_submix_original](https://huggingface.co/datasets/DataProvenanceInitiative/niv2_submix_original) |
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## Details |
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This dataset contains all coreference examples that were included in the [Flan 2022 collection](https://github.com/google-research/FLAN/tree/main/flan/v2) which were orignally published in Super-Natural-Instructions. |
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The data is copied from the preprocessed Natural Instructions v2 dataset at [DataProvenanceInitiative/niv2_submix_original](https://huggingface.co/datasets/DataProvenanceInitiative/niv2_submix_original). |
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These tasks are: |
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* "task1391_winogrande_coreference_resolution" |
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* "task1664_wino_bias_coreference_resolution" |
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* "task304_numeric_fused_head_coreference_resolution" |
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* "task892_gap_coreference_resolution" |
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* "task891_gap_coreference_resolution" |
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* "task330_gap_coreference_resolution" |
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* "task401_numeric_fused_head_coreference_resolution" |
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* "task033_winogrande_coreference_resolution" |
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* "task133_winowhy_coreference_resolution" |
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* "task329_gap_coreference_resolution" |
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* "task249_enhanced_wsc_coreference_resolution" |
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* "task648_winograd_wsc_coreference_resolution" |
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* "task1390_wsc_fiexed_coreference_resolution" |
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* "task893_gap_coreference_resolution" |
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### Fields |
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- `inputs`: a `string` feature. |
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- `targets`: a `string` feature. |
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- `task_source`: a `string` feature. |
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- `task_name`: a `string` feature. |
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- `template_type`: a `string` feature. |
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## Citation |
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``` |
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@inproceedings{wang-etal-2022-super, |
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title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks", |
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author = "Wang, Yizhong and |
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Mishra, Swaroop and |
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Alipoormolabashi, Pegah and |
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Kordi, Yeganeh and |
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Mirzaei, Amirreza and |
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Naik, Atharva and |
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Ashok, Arjun and |
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Dhanasekaran, Arut Selvan and |
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Arunkumar, Anjana and |
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Stap, David and |
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Pathak, Eshaan and |
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Karamanolakis, Giannis and |
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Lai, Haizhi and |
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Purohit, Ishan and |
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Mondal, Ishani and |
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Anderson, Jacob and |
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Kuznia, Kirby and |
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Doshi, Krima and |
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Pal, Kuntal Kumar and |
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Patel, Maitreya and |
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Moradshahi, Mehrad and |
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Parmar, Mihir and |
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Purohit, Mirali and |
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Varshney, Neeraj and |
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Kaza, Phani Rohitha and |
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Verma, Pulkit and |
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Puri, Ravsehaj Singh and |
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Karia, Rushang and |
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Doshi, Savan and |
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Sampat, Shailaja Keyur and |
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Mishra, Siddhartha and |
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Reddy A, Sujan and |
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Patro, Sumanta and |
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Dixit, Tanay and |
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Shen, Xudong", |
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editor = "Goldberg, Yoav and |
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Kozareva, Zornitsa and |
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Zhang, Yue", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, United Arab Emirates", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.emnlp-main.340", |
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doi = "10.18653/v1/2022.emnlp-main.340", |
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pages = "5085--5109", |
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abstract = "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{---}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.", |
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} |
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``` |
