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Found 4 papers, 2 papers with code
New or Old? Exploring How Pre-Trained Language Models Represent Discourse Entities
Recent research shows that pre-trained language models, built to generate text conditioned on some context, learn to encode syntactic knowledge to a certain degree.
Neural Conversation Models and How to Rein Them in: A Survey of Failures and Fixes
In this survey, we interpret Grice's maxims of cooperative conversation from the perspective of this specific research area and systematize the literature under the aspect of what makes a contribution appropriate: A neural conversation model has to be fluent, informative, consistent, coherent, and follow social norms.
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models
Coherent discourse is distinguished from a mere collection of utterances by the satisfaction of a diverse set of constraints, for example choice of expression, logical relation between denoted events, and implicit compatibility with world-knowledge.
Embedding Space Correlation as a Measure of Domain Similarity
Prior work has determined domain similarity using text-based features of a corpus.
