Text Anomaly Detection with ARAE-AnoGAN

Generative adversarial networks (GANs) are now one of the key techniques for detecting anomalies in images, yielding remarkable results. Applying similar methods to discrete structures, such as text sequences, is still largely an unknown. In this work, we introduce a new GAN-based text anomaly detection method, called ARAE-AnoGAN, that trains an adversarially regularized autoencoder (ARAE) to reconstruct normal sentences and detects anomalies via a combined anomaly score based on the building blocks of ARAE. Finally, we present experimental results demonstrating the effectiveness of ARAE-AnoGAN and other deep learning methods in text anomaly detection

MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space

As an essential step towards computer creativity, automatic poetry generation has gained increasing attention these years. Though recent neural models make prominent progress in some criteria of poetry quality, generated poems still suffer from the problem of poor diversity. Related literature researches show that different factors, such as life experience, historical background, etc., would influence composition styles of poets, which considerably contributes to the high diversity of human-authored poetry. Inspired by this, we propose MixPoet, a novel model that absorbs multiple factors to create various styles and promote diversity. Based on a semi-supervised variational autoencoder, our model disentangles the latent space into some subspaces, with each conditioned on one influence factor by adversarial training. In this way, the model learns a controllable latent variable to capture and mix generalized factor-related properties. Different factor mixtures lead to diverse styles and hence further differentiate generated poems from each other. Experiment results on Chinese poetry demonstrate that MixPoet improves both diversity and quality against three state-of-the-art models.Comment: 8 pages, 5 figures, published in AAAI 202

Dialog Response Generation Using Adversarially Learned Latent Bag-of-Words

Dialog response generation is the task of generating response utterance given a query utterance. Apart from generating relevant and coherent responses, one would like the dialog generation model to generate diverse and informative sentences. In this work, we propose and explore a novel multi-stage dialog response generation approach. In the first stage of our proposed multi-stage approach, we construct a variational latent space on the bag-of-words representation of the query and response utterances. In the second stage, transformation from query latent code to response latent code is learned using an adversarial process. The final stage involves fine-tuning a pretrained transformer based model called text-to-text transfer (T5) (Raffel et al., 2019) using a novel training regimen to generate the response utterances by conditioning on the query utterance and the response word learned in the previous stage. We evaluate our proposed approach on two popular dialog datasets. Our proposed approach outperforms the baseline transformer model on multiple quantitative metrics including overlap metric (Bleu), diversity metrics (distinct-1 and distinct-2), and fluency metric (perplexity)