论文标题
最小化联合实体中SEQ2SEQ模型的暴露偏差和关系提取
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
论文作者
论文摘要
联合实体和关系提取旨在直接从纯文本中提取关系三重态。先前的工作利用序列到序列（SEQ2SEQ）模型的三胞胎序列产生。但是，SEQ2SEQ在无序的三胞胎上执行了不必要的顺序，并涉及与误差累积相关的较大的解码长度。这些引入了暴露偏见，这可能会导致模型过度拟合频繁的标签组合，从而恶化概括。我们提出了一个新型的序列到未排序的摩尔特树（SEQ2UMTREE）模型，以通过将解码长度限制为三胞胎内的三个，并在三重态之间删除秩序，从而最大程度地减少暴露偏差的影响。我们在两个数据集（DUIE和NYT）上评估了我们的模型，并系统地研究了暴露偏见如何改变SEQ2SEQ模型的性能。实验表明，最新的SEQ2SEQ模型过度拟合了两个数据集，而SEQ2UMTREE显示出明显更好的概括。我们的代码可在https://github.com/windchimeran/openjere上找到。
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 https://github.com/WindChimeRan/OpenJERE .
