Suicide is an important public health concern and one of the leading causes
of death worldwide. Suicidal behaviors, including suicide attempts (SA) and
suicide ideations (SI), are leading risk factors for death by suicide.
Information related to patients' previous and current SA and SI are frequently
documented in the electronic health record (EHR) notes. Accurate detection of
such documentation may help improve surveillance and predictions of patients'
suicidal behaviors and alert medical professionals for suicide prevention
efforts. In this study, we first built Suicide Attempt and Ideation Events
(ScAN) dataset, a subset of the publicly available MIMIC III dataset spanning
over 12k+ EHR notes with 19k+ annotated SA and SI events information. The
annotations also contain attributes such as method of suicide attempt. We also
provide a strong baseline model ScANER (Suicide Attempt and Ideation Events
Retriever), a multi-task RoBERTa-based model with a retrieval module to extract
all the relevant suicidal behavioral evidences from EHR notes of an
hospital-stay and, and a prediction module to identify the type of suicidal
behavior (SA and SI) concluded during the patient's stay at the hospital.
ScANER achieved a macro-weighted F1-score of 0.83 for identifying suicidal
behavioral evidences and a macro F1-score of 0.78 and 0.60 for classification
of SA and SI for the patient's hospital-stay, respectively. ScAN and ScANER are
publicly available.

本研究利用电子病历记录中的数据建立了一个包含自杀企图和自杀意念信息的数据集 (Suicide Attempt and Ideation Events (ScAN))，并利用 RoBERTa 多任务模型设计了一个名为 ScANER 的检索模型实现了相关自杀行为信息的提取，同时可以对患者的自杀行为类型进行分类，该模型取得了比较高的性能表现 (0.83 的加权 F1 值)，ScAN 和 ScANER 模型均可以公开使用。

ScAN：自杀企图与自杀意念事件数据集

ScAN: Suicide Attempt and Ideation Events Dataset

Systematic compositionality is an essential mechanism in human language,
allowing the recombination of known parts to create novel expressions. However,
existing neural models have been shown to lack this basic ability in learning
symbolic structures. Motivated by the failure of a Transformer model on the
SCAN compositionality challenge (Lake and Baroni, 2018), which requires parsing
a command into actions, we propose two auxiliary sequence prediction tasks that
track the progress of function and argument semantics, as additional training
supervision. These automatically-generated sequences are more representative of
the underlying compositional symbolic structures of the input data. During
inference, the model jointly predicts the next action and the next tokens in
the auxiliary sequences at each step. Experiments on the SCAN dataset show that
our method encourages the Transformer to understand compositional structures of
the command, improving its accuracy on multiple challenging splits from <= 10%
to 100%. With only 418 (5%) training instances, our approach still achieves
97.8% accuracy on the MCD1 split. Therefore, we argue that compositionality can
be induced in Transformers given minimal but proper guidance. We also show that
a better result is achieved using less contextualized vectors as the
attention's query, providing insights into architecture choices in achieving
systematic compositionality. Finally, we show positive generalization results
on the groundedSCAN task (Ruis et al., 2020). Our code is publicly available
at: this https URL

该论文提出了两种辅助序列预测任务来帮助解决神经模型在学习符号结构时的不足，并通过在 SCAN 数据集上的实验证明了所提出的方法可以提高 Transformer 模型理解命令的组合结构的能力。

通过辅助序列预测任务诱导 Transformer 的组合泛化能力

Inducing Transformer's Compositional Generalization Ability via  Auxiliary Sequence Prediction Tasks

Lake and Baroni (2018) introduced the SCAN dataset probing the ability of
seq2seq models to capture compositional generalizations, such as inferring the
meaning of "jump around" 0-shot from the component words. Recurrent networks
(RNNs) were found to completely fail the most challenging generalization cases.
We test here a convolutional network (CNN) on these tasks, reporting hugely
improved performance with respect to RNNs. Despite the big improvement, the CNN
has however not induced systematic rules, suggesting that the difference
between compositional and non-compositional behaviour is not clear-cut.

本文介绍了 SCAN 数据集，研究了 seq2seq 模型在捕捉组合概括（如从组成词中推断 “jump around” 的含义）方面的能力，发现循环神经网络（RNNs）在最具挑战性的概括情况下完全失败，进而测试了卷积神经网络（CNN）的性能，结果显示与 RNN 相比有了巨大的提高，但该模型仍然没有引发系统性规则，这表明组合和非组合行为之间的区别并不明显。

CNNs 比 RNNs 更灵活地跳跃：序列到序列卷积网络中的组合概括

CNNs found to jump around more skillfully than RNNs: Compositional  generalization in seq2seq convolutional networks

Lake and Baroni (2018) recently introduced the SCAN data set, which consists
of simple commands paired with action sequences and is intended to test the
strong generalization abilities of recurrent sequence-to-sequence models. Their
initial experiments suggested that such models may fail because they lack the
ability to extract systematic rules. Here, we take a closer look at SCAN and
show that it does not always capture the kind of generalization that it was
designed for. To mitigate this we propose a complementary dataset, which
requires mapping actions back to the original commands, called NACS. We show
that models that do well on SCAN do not necessarily do well on NACS, and that
NACS exhibits properties more closely aligned with realistic use-cases for
sequence-to-sequence models.

研究对比分析了两个数据集（SCAN 和 NACS）对循环编码器的细致测试，结果表明在 NACS 上表现良好的模型未必在 SCAN 上也表现良好，这为理解序列到序列模型的泛化特性提供了新视角。