Search Results for author:
Found 10 papers, 8 papers with code
Small Models Are (Still) Effective Cross-Domain Argument Extractors
Effective ontology transfer has been a major goal of recent work on event argument extraction (EAE).
Event-Keyed Summarization
We introduce event-keyed summarization (EKS), a novel task that marries traditional summarization and document-level event extraction, with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure.
MultiMUC: Multilingual Template Filling on MUC-4
We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian.
FAMuS: Frames Across Multiple Sources
Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents.
A Unified View of Evaluation Metrics for Structured Prediction
We present a conceptual framework that unifies a variety of evaluation metrics for different structured prediction tasks (e. g. event and relation extraction, syntactic and semantic parsing).
On Event Individuation for Document-Level Information Extraction
As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE.
Iterative Document-level Information Extraction via Imitation Learning
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i. e., N-tuples representing a mapping from named slots to spans of text) within a document.
Argument Linking: A Survey and Forecast
Semantic role labeling (SRL) -- identifying the semantic relationships between a predicate and other constituents in the same sentence -- is a well-studied task in natural language understanding (NLU).
Decomposing and Recomposing Event Structure
We present an event structure classification empirically derived from inferential properties annotated on sentence- and document-level Universal Decompositional Semantics (UDS) graphs.
Natural Language Inference with Mixed Effects
There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating those annotations to a single label problematic.
