We have noticed that Marek et al. (2021) try to re-implement our paper Zheng
et al. (2020a) in their work "OodGAN: Generative Adversarial Network for
Out-of-Domain Data Generation". Our paper proposes a model to generate pseudo
OOD samples that are akin to IN-Domain (IND) input utterances. These pseudo OOD
samples can be used to improve the OOD detection performance by optimizing an
entropy regularization term when building the IND classifier. Marek et al.
(2021) report a large gap between their re-implemented results and ours on the
CLINC150 dataset (Larson et al., 2019). This paper discusses some key
observations that may have led to such a large gap. Most of these observations
originate from our experiments because Marek et al. (2021) have not released
their codes1. One of the most important observations is that stronger IND
classifiers usually exhibit a more robust ability to detect OOD samples. We
hope these observations help other researchers, including Marek et al. (2021),
to develop better OOD detectors in their applications.

本论文探讨了利用生成对抗网络生成伪 OOD 样本来提高基于 IND 分类器对 OOD 样本检测性能的方法，并通过熵正则化项进行优化，同时观察研究表明更强的 IND 分类器能更好地识别 OOD 样本，希望这些发现可对其他研究人员提供有益的帮助。

在構建越域檢測器時，正確分類預測樣本至關重要：對 Marek 等人（2021）的答覆

Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)

Natural Language Understanding (NLU) is a vital component of dialogue
systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in
practical applications, since the acceptance of the OOD input that is
unsupported by the current system may lead to catastrophic failure. However,
most existing OOD detection methods rely heavily on manually labeled OOD
samples and cannot take full advantage of unlabeled data. This limits the
feasibility of these models in practical applications.
In this paper, we propose a novel model to generate high-quality pseudo OOD
samples that are akin to IN-Domain (IND) input utterances, and thereby improves
the performance of OOD detection. To this end, an autoencoder is trained to map
an input utterance into a latent code. and the codes of IND and OOD samples are
trained to be indistinguishable by utilizing a generative adversarial network.
To provide more supervision signals, an auxiliary classifier is introduced to
regularize the generated OOD samples to have indistinguishable intent labels.
Experiments show that these pseudo OOD samples generated by our model can be
used to effectively improve OOD detection in NLU. Besides, we also demonstrate
that the effectiveness of these pseudo OOD data can be further improved by
efficiently utilizing unlabeled data.

本文提出了一种基于自动编码器和生成对抗网络生成伪 OOD 样本来提高 NLU 中 OOD 检测性能的新模型，同时也演示了未标注数据可以进一步提高这些伪 OOD 数据的有效性。