39 citations · 43 across the 5 of their papers we have counts for
6 papers
Towards Understanding What Code Language Models Learned
Toufique Ahmed, Dian Yu, Chengxuan Huang +3
Pre-trained language models are effective in a variety of natural language tasks, but it has been argued their capabilities fall short of fully learning meaning or understanding la…
Automatically Exposing Problems with Neural Dialog Models
Dian Yu, Kenji Sagae
Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly m…
Language Embeddings for Typology and Cross-lingual Transfer Learning
Dian Yu, Taiqi He, Kenji Sagae
Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relatio…
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models
Dian Yu, Zhou Yu, Kenji Sagae
Large language models benefit from training with a large amount of unlabeled text, which gives them increasingly fluent and diverse generation capabilities. However, using these mo…
Studying the Difference Between Natural and Programming Language Corpora
Casey Casalnuovo, Kenji Sagae, Prem Devanbu
Code corpora, as observed in large software systems, are now known to be far more repetitive and predictable than natural language corpora. But why? Does the difference simply aris…
Fast Rhetorical Structure Theory Discourse Parsing
Michael Heilman, Kenji Sagae
In recent years, There has been a variety of research on discourse parsing, particularly RST discourse parsing. Most of the recent work on RST parsing has focused on implementing n…