  Large language models have ushered in a golden age of semantic parsing. The
seq2seq paradigm allows for open-schema and abstractive attribute and relation
extraction given only small amounts of finetuning data. Language model
pretraining has simultaneously enabled great strides in natural language
inference, reasoning about entailment and implication in free text. These
advances motivate us to construct ImPaKT, a dataset for open-schema information
extraction, consisting of around 2500 text snippets from the C4 corpus, in the
shopping domain (product buying guides), professionally annotated with
extracted attributes, types, attribute summaries (attribute schema discovery
from idiosyncratic text), many-to-one relations between compound and atomic
attributes, and implication relations. We release this data in hope that it
will be useful in fine tuning semantic parsers for information extraction and
knowledge base construction across a variety of domains. We evaluate the power
of this approach by fine-tuning the open source UL2 language model on a subset
of the dataset, extracting a set of implication relations from a corpus of
product buying guides, and conducting human evaluations of the resulting
predictions.
