Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model
نویسندگان
چکیده
منابع مشابه
Causal Effect Inference with Deep Latent-Variable Models
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational st...
متن کاملRelational Deep Learning: A Deep Latent Variable Model for Link Prediction
Link prediction is a fundamental task in such areas as social network analysis, information retrieval, and bioinformatics. Usually link prediction methods use the link structures or node attributes as the sources of information. Recently, the relational topic model (RTM) and its variants have been proposed as hybrid methods that jointly model both sources of information and achieve very promisi...
متن کاملText Generation Based on Generative Adversarial Nets with Latent Variable
In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative adversarial net. The use of high-level latent random variables is helpful to learn the data distribution and solve the problem that generative adversarial net always emits the similar data. We propose the VGA...
متن کاملA Latent-Variable Lattice Model
Markov random field (MRF) learning is intractable, and the approximation algorithms are computationally expensive. Since only a small subset of MRF is used frequently in computer vision, we characterize this subset with three concepts: (1) Lattice, (2) Homogeneity, and (3) Inertia; and design a non-markov high-bias low-variance model as an alternative to this subclass of MRF. Our goal is robust...
متن کاملA Knowledge-Grounded Neural Conversation Model
Neural network models are capable of generating extremely natural sounding conversational interactions. Nevertheless, these models have yet to demonstrate that they can incorporate content in the form of factual information or entity-grounded opinion that would enable them to serve in more task-oriented conversational applications. This paper presents a novel, fully data-driven, and knowledge-g...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Speech & Language
سال: 2020
ISSN: 0885-2308
DOI: 10.1016/j.csl.2020.101069