Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification
Authors: Zhong Qian, Peifeng Li, Yue Zhang, Guodong Zhou, Qiaoming Zhu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 qianzhongqz@163.com, pfli@suda.edu.cn, yue zhang@sutd.edu.sg, {gdzhou, qmzhu}@suda.edu.cn |
| 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. |