Two Discourse Driven Language Models for Semantics
This work addresses semantic natural language processing tasks, but it is incremental as it builds on previous proposals and uses existing methods like N-gram and neural models.
The authors tackled the problem of modeling semantic knowledge for natural language understanding by developing two language models that capture semantic frame chains and discourse information, showing that these models improve performance on co-reference resolution and discourse parsing tasks.
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. The quality of the semantic language models (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.