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Large language models have ushered in a golden age of semantic parsing. Theseq2seq paradigm allows for open-schema and abstractive attribute and relationextraction given only small amounts of finetuning data. Language modelpretraining has simultaneously enabled great strides in natural languageinference, reasoning about entailment and implication in free text. Theseadvances motivate us to construct ImPaKT, a dataset for open-schema informationextraction, consisting of around 2500 text snippets from the C4 corpus, in theshopping domain (product buying guides), professionally annotated withextracted attributes, types, attribute summaries (attribute schema discoveryfrom idiosyncratic text), many-to-one relations between compound and atomicattributes, and implication relations. We release this data in hope that itwill be useful in fine tuning semantic parsers for information extraction andknowledge base construction across a variety of domains. We evaluate the powerof this approach by fine-tuning the open source UL2 language model on a subsetof the dataset, extracting a set of implication relations from a corpus ofproduct buying guides, and conducting human evaluations of the resultingpredictions.

ImPaKT: A Dataset for Open-Schema Knowledge Base Construction