Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning

Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the content and the attributes, and 2) troublesome adversarial learning. In this paper, we show that neither of these components is indispensable. We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer. Our method consists of three key components: a variational auto-encoder (VAE), some attribute predictors (one for each attribute), and a content predictor. The VAE and the two types of predictors enable us to perform gradient-based optimization in the continuous space, which is mapped from sentences in a discrete space, to find the representation of a target sentence with the desired attributes and preserved content. Moreover, the proposed method naturally has the ability to simultaneously manipulate multiple fine-grained attributes, such as sentence length and the presence of specific words, when performing text style transfer tasks. Compared with previous adversarial learning based methods, the proposed method is more interpretable, controllable and easier to train. Extensive experimental studies on three popular text style transfer tasks show that the proposed method significantly outperforms five state-of-the-art methods.Comment: Association for the Advancement of Artificial Intelligence. AAAI 202

Determining the core radio luminosity function of radio AGNs via copula

The radio luminosity functions (RLFs) of active galactic nuclei (AGNs) are traditionally measured based on total emission, which doesn't reflect the current activity of the central black hole. The increasing interest in compact radio cores of AGNs requires determination of the RLF based on core emission (i.e., core RLF). In this work we have established a large sample (totaling 1207) of radio-loud AGNs, mainly consisting of radio galaxies (RGs) and steep-spectrum radio quasars (SSRQs). Based on the sample, we explore the relationship between core luminosity ($L_c$) and total luminosity ($L_t$) via a powerful statistical tool called "Copula". The conditional probability distribution $p(\log L_{c} \mid \log L_{t})$ is obtained. We derive the core RLF as a convolution of $p(\log L_{c} \mid \log L_{t})$ with the total RLF which was determined by previous work. We relate the separate RG and SSRQ core RLFs via a relativistic beaming model and find that SSRQs have an average Lorentz factor of $\gamma=9.84_{-2.50}^{+3.61}$, and that most are seen within $8^{\circ} \lesssim \theta \lesssim 45^{\circ}$ of the jet axis. Compared with the total RLF which is mainly contributed by extended emission, the core RLF shows a very weak luminosity-dependent evolution, with the number density peaking around $z\thicksim 0.8$ for all luminosities. Differences between core and total RLFs can be explained in a framework involving a combination of density and luminosity evolutions where the cores have significantly weaker luminosity evolution than the extended emission.Comment: Accepted for publication in the ApJ

Information-Theoretic Limits on Compression of Semantic Information

As conventional communication systems based on classic information theory have closely approached the limits of Shannon channel capacity, semantic communication has been recognized as a key enabling technology for the further improvement of communication performance. However, it is still unsettled on how to represent semantic information and characterise the theoretical limits. In this paper, we consider a semantic source which consists of a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network. Then we give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence. We further characterise the limits on lossy compression of the semantic source and the corresponding upper and lower bounds of the rate-distortion function. We also investigate the lossy compression of the semantic source with side information at both the encoder and decoder, and obtain the rate distortion function. We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information