Two Discourse Driven Language Models for Semantics

Abstract: 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 LLM 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 LLM and three discriminatively trained "neural" LLMs that generate embeddings for semantic frames. The quality of the semantic LLMs (SemLM) is evaluated both intrinsically, using perplexity and a narrative cloze test and extrinsically - we show that our SemLM helps improve performance on semantic natural language processing tasks such as co-reference resolution and discourse parsing.