100 citations · 117 across the 3 of their papers we have counts for
13 papers
Controlling the Focus of Pretrained Language Generation Models
Jiabao Ji, Yoon Kim, James Glass +1
The finetuning of pretrained transformer-based language generation models are typically conducted in an end-to-end manner, where the model learns to attend to relevant parts of the…
Co-training Improves Prompt-based Learning for Large Language Models
Hunter Lang, Monica Agrawal, Yoon Kim +1
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data. While prompting has emerged as a promising par…
Compound Probabilistic Context-Free Grammars for Grammar Induction
Yoon Kim, Chris Dyer, Alexander M. Rush
We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar. In contrast to traditional form…
Amortized Bethe Free Energy Minimization for Learning MRFs
Sam Wiseman, Yoon Kim
We propose to learn deep undirected graphical models (i.e., MRFs) with a non-ELBO objective for which we can calculate exact gradients. In particular, we optimize a saddle-point ob…
Unsupervised Recurrent Neural Network Grammars
Yoon Kim, Alexander M. Rush, Lei Yu +3
Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence i…
A Tutorial on Deep Latent Variable Models of Natural Language
Yoon Kim, Sam Wiseman, Alexander M. Rush
There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning. Latent variable modeling makes it easy to explicitly…