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.

通过提出一种名为 S2F-NER 的新的 Sequence-to-Forest 生成模型，我们的研究论文在处理复杂的、嵌套的、重叠的实体时显著提高了准确性，比传统的 Seq2Seq 和基于跨度的分类方法更适应于长文本数据集，并且能有效解决曝光偏差问题。

S2F-NER：探索用于复杂实体识别的序列生成森林

S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity  Recognition

Information Extraction from scientific literature can be challenging due to
the highly specialised nature of such text. We describe our entity recognition
methods developed as part of the DEAL (Detecting Entities in the Astrophysics
Literature) shared task. The aim of the task is to build a system that can
identify Named Entities in a dataset composed by scholarly articles from
astrophysics literature. We planned our participation such that it enables us
to conduct an empirical comparison between word-based tagging and span-based
classification methods. When evaluated on two hidden test sets provided by the
organizer, our best-performing submission achieved $F_1$ scores of 0.8307
(validation phase) and 0.7990 (testing phase).

本研究旨在构建一个可从天体物理学文献中识别命名实体的系统，并比较基于单词标记和基于跨度分类的方法，在验证和测试中最好的提交均进行了评估，得分分别为 0.8307 和 0.7990。

天体物理学文献中的实体检测：基于词和基于范围的实体识别方法比较

Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods

Recent works that revealed the vulnerability of dialogue state tracking (DST)
models to distributional shifts have made holistic comparisons on robustness
and qualitative analyses increasingly important for understanding their
relative performance. We present our findings from standardized and
comprehensive DST diagnoses, which have previously been sparse and
uncoordinated, using our toolkit, CheckDST, a collection of robustness tests
and failure mode analytics. We discover that different classes of DST models
have clear strengths and weaknesses, where generation models are more promising
for handling language variety while span-based classification models are more
robust to unseen entities. Prompted by this discovery, we also compare
checkpoints from the same model and find that the standard practice of
selecting checkpoints using validation loss/accuracy is prone to overfitting
and each model class has distinct patterns of failure. Lastly, we demonstrate
how our diagnoses motivate a pre-finetuning procedure with non-dialogue data
that offers comprehensive improvements to generation models by alleviating the
impact of distributional shifts through transfer learning.

通过我们的工具包 CheckDST，我们进行了标准化和全面的 DST 诊断，发现了不同类别的 DST 模型具有明显的优势和劣势，生成模型更适用于语言多样性，而基于跨度分类的模型对不可见实体更具鲁棒性，我们还发现了各个模型的失败模式，并通过预微调过程改善了生成模型的分布偏差问题。