Named entity recognition (NER) remains challenging when entity mentions can
be discontinuous. Existing methods break the recognition process into several
sequential steps. In training, they predict conditioned on the golden
intermediate results, while at inference relying on the model output of the
previous steps, which introduces exposure bias. To solve this problem, we first
construct a segment graph for each sentence, in which each node denotes a
segment (a continuous entity on its own, or a part of discontinuous entities),
and an edge links two nodes that belong to the same entity. The nodes and edges
can be generated respectively in one stage with a grid tagging scheme and
learned jointly using a novel architecture named Mac. Then discontinuous NER
can be reformulated as a non-parametric process of discovering maximal cliques
in the graph and concatenating the spans in each clique. Experiments on three
benchmarks show that our method outperforms the state-of-the-art (SOTA)
results, with up to 3.5 percentage points improvement on F1, and achieves 5x
speedup over the SOTA model.

本文提出了一种基于 Mac 模型的分段图算法，用于解决实体识别中异构实体的问题。该算法可将异构实体视作图中的节点，通过识别图中的最大团并连接团内的连续实体段，实现对异构实体的非参数化处理，在三个基准数据集上的结果证明该方法领先于目前的最优算法，并且速度提高了 5 倍。

作为最大团探测的间断命名实体识别

Discontinuous Named Entity Recognition as Maximal Clique Discovery

This paper presents a novel attention-based algorithm for achieving adaptive
computation called DACT, which, unlike existing ones, is end-to-end
differentiable. Our method can be used in conjunction with many networks; in
particular, we study its application to the widely known MAC architecture,
obtaining a significant reduction in the number of recurrent steps needed to
achieve similar accuracies, therefore improving its performance to computation
ratio. Furthermore, we show that by increasing the maximum number of steps
used, we surpass the accuracy of even our best non-adaptive MAC in the CLEVR
dataset, demonstrating that our approach is able to control the number of steps
without significant loss of performance. Additional advantages provided by our
approach include considerably improving interpretability by discarding useless
steps and providing more insights into the underlying reasoning process.
Finally, we present adaptive computation as an equivalent to an ensemble of
models, similar to a mixture of expert formulation. Both the code and the
configuration files for our experiments are made available to support further
research in this area.

本文介绍了一种新颖的基于注意力机制的自适应计算算法，称为 DACT，与现有算法不同，是端到端可微的。该算法可以与许多网络配合使用；特别地，我们研究了其在广泛使用的 MAC 体系结构中的应用，使所需的循环步骤数量显著减少，从而改善了其性能与计算比率。此外，我们还表明，通过增加所使用的最大步骤数，即使在 CLEVR 数据集中也能超越我们最佳的非自适应 MAC 的准确性，证明我们的方法能够控制步骤数而不会显著降低性能。我们的方法提供的额外优点包括通过丢弃无用的步骤，并提供对底层推理过程的更多见解，从而显着提高了可解释性。最后，我们将自适应计算呈现为模型集成的等价形式，类似于专家混合配方。我们的实验代码和配置文件均可用于支持这一领域的进一步研究。