2024 COLING COLING 2024

ReproHum #0712-01: Human Evaluation Reproduction Report for “Hierarchical Sketch Induction for Paraphrase Generation”

Abstract

AbstractHuman evaluations are indispensable in the development of NLP systems because they provide direct insights into how effectively these systems meet real-world needs and expectations. Ensuring the reproducibility of these evaluations is vital for maintaining credibility in natural language processing research. This paper presents our reproduction of the human evaluation experiments conducted by Hosking et al. (2022) for their paraphrase generation approach. Through careful replication we found that our results closely align with those in the original study, indicating a high degree of reproducibility.

🧭 Keyword Pioneer — hierarchical sketch induction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio