**RotatE** is a method for generating graph embeddings which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. The RotatE model is trained using a [self-adversarial negative sampling](https://paperswithcode.com/method/self-adversarial-negative-sampling) technique.

**ALAE**, or **Adversarial Latent Autoencoder**, is a type of autoencoder that attempts to overcome some of the limitations of[ generative adversarial networks](https://paperswithcode.com/paper/generative-adversarial-networks). The architecture allows the latent distribution to be learned from data to address entanglement (A). The output data distribution is learned with an adversarial strategy (B). Thus, we retain the generative properties of GANs, as well as the ability to build on the recent advances in this area. For instance, we can include independent sources of stochasticity, which have proven essential for generating image details, or can leverage recent improvements on GAN loss functions, regularization, and hyperparameters tuning. Finally, to implement (A) and (B), AE reciprocity is imposed in the latent space (C). Therefore, we can avoid using reconstruction losses based on simple $\mathcal{l}\){2}$ norm that operates in data space, where they are often suboptimal, like for the image space. Since it works on the latent space, rather than autoencoding the data space, the approach is named Adversarial Latent Autoencoder (ALAE).

ALAE

Adversarial Latent Autoencoders

RotatE

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

**Review-guided Answer Helpfulness Prediction** (RAHP) is a textual inference model for identifying helpful answers in e-commerce. It not only considers the interactions between QA pairs, but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers.

Source	RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com