research-backup/roberta-large-conceptnet-average-no-mask-prompt-a-nce
Feature Extraction
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Updated
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The selected subset of ConceptNet used in this work, which compiled
to fine-tune RelBERT model.
We removed NotCapableOf
and NotDesires
to keep the positive relation only.
We consider the original test set as test set, dev1 as the training set, and dev2 as the validation set.
An example of train
looks as follows.
{
"relation_type": "AtLocation",
"positives": [["fish", "water"], ["cloud", "sky"], ["child", "school"], ... ],
"negatives": [["pen", "write"], ["sex", "fun"], ["soccer", "sport"], ["fish", "school"], ... ]
}
train | validation | test |
---|---|---|
28 | 34 | 16 |
@InProceedings{P16-1137,
author = "Li, Xiang
and Taheri, Aynaz
and Tu, Lifu
and Gimpel, Kevin",
title = "Commonsense Knowledge Base Completion",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ",
year = "2016",
publisher = "Association for Computational Linguistics",
pages = "1445--1455",
location = "Berlin, Germany",
doi = "10.18653/v1/P16-1137",
url = "http://aclweb.org/anthology/P16-1137"
}