Joint entity and relation extraction aims to extract relation triplets from
plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models
for triplet sequence generation. However, Seq2Seq enforces an unnecessary order
on the unordered triplets and involves a large decoding length associated with
error accumulation. These introduce exposure bias, which may cause the models
overfit to the frequent label combination, thus deteriorating the
generalization. We propose a novel Sequence-to-Unordered-Multi-Tree
(Seq2UMTree) model to minimize the effects of exposure bias by limiting the
decoding length to three within a triplet and removing the order among
triplets. We evaluate our model on two datasets, DuIE and NYT, and
systematically study how exposure bias alters the performance of Seq2Seq
models. Experiments show that the state-of-the-art Seq2Seq model overfits to
both datasets while Seq2UMTree shows significantly better generalization. Our
code is available at this https URL .

提出了一种 Sequence-to-Unordered-Multi-Tree 模型用于联合实体和关系提取，通过限制三元组内的解码长度和移除三元组之间的顺序等方法，最小化暴露偏差效应并提高泛化能力，实验表明该模型在 DuIE 和 NYT 数据集上取得了比 Seq2Seq 更好的效果。