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.

本文介绍了一种新颖的基于序列到序列 (seq2seq) 的少样本化精细颗粒化化学实体抽取方法 Chem-FINESE，其包括一个用于从输入句子中提取命名实体的 seq2seq 实体抽取器和一个用于从提取的实体重构原始输入句子的 seq2seq 自我验证模块。通过在少样本设置下使用 ChemNER + 和 CHEMET 数据集进行实验，我们的新框架相对于已有模型取得了高达 8.26% 和 6.84% 的绝对 F1 得分提升。