Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog
system. Most dialog systems are trained on a pool of annotated OOD data to
achieve this goal. However, collecting the annotated OOD data for a given
domain is an expensive process. To mitigate this issue, previous works have
proposed generative adversarial networks (GAN) based models to generate OOD
data for a given domain automatically. However, these proposed models do not
work directly with the text. They work with the text's latent space instead,
enforcing these models to include components responsible for encoding text into
latent space and decoding it back, such as auto-encoder. These components
increase the model complexity, making it difficult to train. We propose OodGAN,
a sequential generative adversarial network (SeqGAN) based model for OOD data
generation. Our proposed model works directly on the text and hence eliminates
the need to include an auto-encoder. OOD data generated using OodGAN model
outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative
improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR
0.95) (Zheng et al., 2020).

使用 SeqGAN 的 OodGAN 模型，可直接处理文本数据而无需附加自编码器组件，生成的 OOD 数据优于 ROSTD 和 OSQ 数据集的现有模型，因其相应的假阳性率的 FPR 0.95 相对提高了 67％和 28％（Zheng et al.，2020）。

OodGAN: 用于域外数据生成的生成对抗网络

OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

Computing devices have recently become capable of interacting with their end
users via natural language. However, they can only operate within a limited
"supported" domain of discourse and fail drastically when faced with an
out-of-domain utterance, mainly due to the limitations of their semantic
parser. In this paper, we propose a semantic parser that generalizes to
out-of-domain examples by learning a general strategy for parsing an unseen
utterance through adapting the logical forms of seen utterances, instead of
learning to generate a logical form from scratch. Our parser maintains a memory
consisting of a representative subset of the seen utterances paired with their
logical forms. Given an unseen utterance, our parser works by looking up a
similar utterance from the memory and adapting its logical form until it fits
the unseen utterance. Moreover, we present a data generation strategy for
constructing utterance-logical form pairs from different domains. Our results
show an improvement of up to 68.8% on one-shot parsing under two different
evaluation settings compared to the baselines.

本文提出了一种语义解析器，通过适应已见过的语句的逻辑形式而不是从头开始生成逻辑形式来学习解析未知语句的一般策略，同时提出了一种数据生成策略来构建不同领域的语句 - 逻辑形式对，结果显示相较于基准测试，单次解析的改善高达 68.8%。