Z
Zhilin Yang
Researcher at Carnegie Mellon University
Publications - 69
Citations - 16355
Zhilin Yang is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Language model. The author has an hindex of 31, co-authored 57 publications receiving 11112 citations. Previous affiliations of Zhilin Yang include Tsinghua University.
Papers
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Proceedings Article
Multi-modal Bayesian embeddings for learning social knowledge graphs
TL;DR: A multi-modal Bayesian embedding model, GenVector, is proposed to learn latent topics that generate word and network embeddings in a shared latent topic space, and significantly decreases the error rate in an online A/B test with live users.
Posted Content
Neural Models for Reasoning over Multiple Mentions using Coreference
TL;DR: This article proposed a coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster and incorporated this layer into a state-of-the-art reading comprehension model.
Posted Content
Semi-Supervised QA with Generative Domain-Adaptive Nets
TL;DR: A novel training framework for semi-supervised question answering is proposed, the Generative Domain-Adaptive Nets, which combines a generative model to generate questions based on the unlabeled text, and combines model- generated questions with human-generated questions for training question answering models.
Posted Content
A Probabilistic Framework for Location Inference from Social Media.
TL;DR: A novel probabilistic model based on factor graphs for location inference that offers several unique advantages for this task is presented and can substantially improve the inference accuracy over that of several state-of-the-art methods.
Proceedings Article
Words or Characters? Fine-grained Gating for Reading Comprehension
TL;DR: The authors proposed a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words for reading comprehension, achieving state-of-the-art results on the Children's Book Test dataset.