S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity
  Recognition
Yongxiu Xu Heyan Huang Yue Hu
Named Entity Recognition (NER) remains challenging due to the complex
entities, like nested, overlapping, and discontinuous entities. Existing
approaches, such as sequence-to-sequence (Seq2Seq) generation and span-based
classification, have shown impressive performance on various NER subtasks, but
they are difficult to scale to datasets with longer input text because of
either exposure bias issue or inefficient computation. In this paper, we
propose a novel Sequence-to-Forest generation paradigm, S2F-NER, which can
directly extract entities in sentence via a Forest decoder that decode multiple
entities in parallel rather than sequentially. Specifically, our model generate
each path of each tree in forest autoregressively, where the maximum depth of
each tree is three (which is the shortest feasible length for complex NER and
is far smaller than the decoding length of Seq2Seq). Based on this novel
paradigm, our model can elegantly mitigates the exposure bias problem and keep
the simplicity of Seq2Seq. Experimental results show that our model
significantly outperforms the baselines on three discontinuous NER datasets and
on two nested NER datasets, especially for discontinuous entity recognition.