100 citations · 117 across the 3 of their papers we have counts for
8 papers · 1 filter
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…
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…
OpenNMT: Neural Machine Translation Toolkit
Guillaume Klein, Yoon Kim, Yuntian Deng +3
OpenNMT is an open-source toolkit for neural machine translation (NMT). The system prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research in…
Adapting Sequence Models for Sentence Correction
Allen Schmaltz, Yoon Kim, Alexander M. Rush +1
In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based…