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

Automatic lyrics generation has received attention from both music and AI
communities for years. Early rule-based approaches have~---due to increases in
computational power and evolution in data-driven models---~mostly been replaced
with deep-learning-based systems. Many existing approaches, however, either
rely heavily on prior knowledge in music and lyrics writing or oversimplify the
task by largely discarding melodic information and its relationship with the
text. We propose an end-to-end melody-conditioned lyrics generation system
based on Sequence Generative Adversarial Networks (SeqGAN), which generates a
line of lyrics given the corresponding melody as the input. Furthermore, we
investigate the performance of the generator with an additional input
condition: the theme or overarching topic of the lyrics to be generated. We
show that the input conditions have no negative impact on the evaluation
metrics while enabling the network to produce more meaningful results.

本文提出了一种基于序列生成对抗网络（SeqGAN）的端到端旋律条件歌词生成系统，可以根据输入的旋律生成一行歌词，并对生成的主题或总体主题进行了探究，并证明输入条件不会对评估度量产生负面影响，同时可以产生更有意义的结果。