  Fine-grained few-shot entity extraction in the chemical domain faces two
unique challenges. First, compared with entity extraction tasks in the general
domain, sentences from chemical papers usually contain more entities. Moreover,
entity extraction models usually have difficulty extracting entities of
long-tailed types. In this paper, we propose Chem-FINESE, a novel
sequence-to-sequence (seq2seq) based few-shot entity extraction approach, to
address these two challenges. Our Chem-FINESE has two components: a seq2seq
entity extractor to extract named entities from the input sentence and a
seq2seq self-validation module to reconstruct the original input sentence from
extracted entities. Inspired by the fact that a good entity extraction system
needs to extract entities faithfully, our new self-validation module leverages
entity extraction results to reconstruct the original input sentence. Besides,
we design a new contrastive loss to reduce excessive copying during the
extraction process. Finally, we release ChemNER+, a new fine-grained chemical
entity extraction dataset that is annotated by domain experts with the ChemNER
schema. Experiments in few-shot settings with both ChemNER+ and CHEMET datasets
show that our newly proposed framework has contributed up to 8.26% and 6.84%
absolute F1-score gains respectively.
