Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing
For researchers in entity typing, this work introduces a method to leverage discourse-level context for long-tail types, though improvements are incremental and substantial room for improvement remains.
Ultra-fine entity typing (UFET) struggles with long-tail types due to reliance on sentence-level context. By generating controlled narratives, Narrative-UFET achieves consistent improvements over sentence-level baselines, with the Change variant providing the strongest signal.
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.