## 2 Related Work
### Generative Adversarial Networks for NLP Tasks  
In this section, we review recent research on discovering rich structure in natural language with variational autoencoders (VAEs) [3] and GANs [4]. Evaluating deep generative models has been challenging so far. To the best of our knowledge, there are very few works on text analysis using GANs. [PERSON] et al. [45] proposed a framework for employing LSTM and CNN for adversarial training to generate realistic text. The latent code \(z\) was fed to the LSTM generator at every time step, while CNN acted as binary sentence classifier which discriminated between real data and generated samples. RNN extensions, such as LSTMs or gated recurrent units (GRUs) that retain long-term memory of tokens, have been shown to work well in practice using maximum likelihood estimation. However, training using maximum likelihood has its downsides, like exposure bias. This refers to the situation in which, during training, the prediction of the next word conditioned on the previous word becomes infeasible since the previous word may not have been seen in the training data. If the generator makes an error early on in the generation process, the generated sentence will keep diverging further away as more words are generated. To solve this issue, a few things have been tried in the past, like scheduled sampling [46] and having task-specific sequence scores. One problem with applying GAN to text is that the gradients from the discriminator cannot properly back-propagate through discrete variables. In [45], this problem was solved by making the word prediction at every time \"soft\" at the word-embedding space. Moreover, [PERSON] et al. [47] proposed bypassing this problem by modeling the generator as a stochastic policy. The reward signal came from the GAN discriminator, judged on a complete sequence, and was passed back to the intermediate state-action steps using Monte-Carlo search. For text, it is possible to create oracle training data from a fixed set of grammars and then evaluate generative models based on whether (or how well) the generated samples agree with the predefined grammar [48]. Considering that it is hard to make a good evaluation for generating text since there is no objective way to assess whether an artificial sentence is more plausible or realistic than another, some works have used BLEU scores of samples on a large amount of unseen test data. The ability to generate similar sentences to unseen real data is considered a measurement of quality [47]. Notably, in terms of sequential data generation with GANs, an alternative approach based on reinforcement learning was used to train the GAN. We are aware of only one preliminary work using GANs to generate continuous-valued sequences, and it aimed to produce polyphonic music using a GAN with an LSTM generator and discriminator [49]. A work with respect to controllable text generation [50] applied the variable autoencoder (VAE) together with controllable information to generate category sentences. Finally, [PERSON] et al. [45] and [PERSON] et al. [51] used GANs for text generation and achieved state-of-the-art results. Finally, another category of approach is the conditional GANs [52] that condition the model on additional information and, therefore, allow us to direct the data generation process. This approach has been mainly used for image generation tasks [53; 5]. Recently, conditional GAN architectures have been also used in NLP, including translation [47] and dialogue generation [54]. After summarizing the work conducted in the area of GANs for NLP tasks, we can observe that even though the research effort has been shifted to GAN approaches for NLP tasks, whose discriminator and generator models are mainly CNNs and/or RNNs (BLSTMs), still a number of limitations and development trends remain unsolved.