6 citations · 7 across the 4 of their papers we have counts for
4 papers
Variational Sequential Labelers for Semi-Supervised Learning
Mingda Chen, Qingming Tang, Karen Livescu +1
We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative l…
Smaller Text Classifiers with Discriminative Cluster Embeddings
Mingda Chen, Kevin Gimpel
Word embedding parameters often dominate overall model sizes in neural methods for natural language processing. We reduce deployed model sizes of text classifiers by learning a har…
Controllable Paraphrase Generation with a Syntactic Exemplar
Mingda Chen, Qingming Tang, Sam Wiseman +1
Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel t…
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
Mingda Chen, Qingming Tang, Sam Wiseman +1
We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We…