Chris Hokamp — accepted AI-conference papers

2025

GLiREL - Generalist Model for Zero-Shot Relation Extraction

Jack Boylan, Chris Hokamp, Demian Gholipour Ghalandari

NAACL 2025long

We introduce GLiREL, an efficient architecture and training paradigm for zero-shot relation classification. Identifying relationships between entities is a key task in information extraction pipelines. The zero-shot setting for relation extraction, where a taxonomy of relations is not pre-specified,…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2022

Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning

Demian Ghalandari, Chris Hokamp, Georgiana Ifrim

ACL 2022long

Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowin…

Cited by 18 Source ↗PDF ↗code ↗Scholar ↗