Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification
Authors: Zhong Qian, Peifeng Li, Yue Zhang, Guodong Zhou, Qiaoming Zhu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Fact Bank show that our method significantly outperforms several stateof-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values. |
| Researcher Affiliation | Academia | Zhong Qian1, Peifeng Li1, Yue Zhang2, Guodong Zhou1, Qiaoming Zhu1 1School of Computer Science and Technology, Soochow University, Suzhou, China 2Singapore University of Technology and Design, Singapore EMAIL, pfli@suda.edu.cn, yue EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of this paper is released at https: //github.com/qz011/ef_ac_gan. |
| Open Datasets | Yes | We evaluate our models on Fact Bank [Saur ı and Pustejovsky, 2009], which contains 3864 sentences and 13506 event factuality values. |
| Dataset Splits | Yes | For fair comparison, we perform 10-fold cross-validation on Fact Bank. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software like Word2Vec and SGD, but does not provide specific version numbers for any libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | For SIP detection we set the dimensions of the POS and hypernym embeddings as 50 and λ = 10 4. For event factuality identification, we set nlstm = 50 and dp = dt = 10. |