Two Discourse Driven Language Models for Semantics

Understand

Natural language understanding often requires deep semantic knowledge.

• Expanding on previous proposals, we suggest that some important aspects of semantic knowledge can be modeled as a language model if done at an appropriate level of abstraction.
• We develop two distinct models that capture semantic frame chains and discourse information while abstracting over the specific mentions of predicates and entities.
• For each model, we investigate four implementations: a "standard" N-gram language model and three discriminatively trained "neural" language models that generate embeddings for semantic frames.