Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing

2026-06-25

AI Summary

This work addresses the limitations of existing ultra-fine entity typing methods, which rely solely on sentence-level context and struggle to accurately classify long-tail entity types. To overcome this, the authors propose a novel approach that leverages controllable generation to produce coherent multi-sentence narratives that expand entity context. They introduce the first Narrative-UFET dataset and design two variants of synthetic narratives—type-preserving and type-altering—to systematically investigate how discourse structure influences entity type disambiguation. Experimental results demonstrate that multi-sentence contexts derived from synthetic narratives substantially outperform sentence-level baselines, particularly for long-tail types. Notably, type-altering narratives provide stronger discriminative signals, and synthetic narratives consistently surpass natural contexts in classification performance, confirming the effectiveness and potential of controllable narrative generation for enhancing ultra-fine-grained entity typing.

Abstract

Ultra-fine entity typing (UFET) assigns highly specific types to entity mentions, but current approaches struggle with types in the long tail. We hypothesize that a key limitation is the reliance on sentence-level context, since disambiguating evidence is often spread across multiple sentences. Testing this has been difficult because all existing UFET resources are sentence-level. We present Narrative-UFET, a controlled extension of UFET in which each entity mention is paired with an automatically generated short, coherent narrative. Synthesizing narratives lets us isolate the effect of specific discourse properties. We experiment with two paired variants: one in which the entity's type is held constant across the narrative (Maintain) and one in which it shifts (Change). We show that narrative context yields consistent improvements on long-tail types over sentence-level baselines, with the Change variant providing the stronger signal. A comparison against naturally occurring contexts shows that synthetic narratives yield stronger gains, indicating that controlled discourse construction can surface signals that real text leaves implicit. Substantial room for improvement remains, suggesting open directions in both discourse modeling and narrative construction.

Problem

• Ultra-fine entity typing
• long-tail types
• discourse context
• narrative generation
• entity disambiguation

Innovation

• Ultra-fine Entity Typing
• Narrative Generation
• Discourse Context
• Long-tail Types
• Controlled Text Synthesis