Story Ending Prediction by Transferable BERT
Authors: Zhongyang Li, Xiao Ding, Ting Liu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this study, we take story ending prediction as the target task to conduct experiments. The final result, an accuracy of 91.8%, dramatically outperforms previous state-of-the-art baseline methods. Several comparative experiments give some helpful suggestions on how to select transfer tasks to improve BERT. |
| Researcher Affiliation | Academia | Zhongyang Li , Xiao Ding and Ting Liu Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology {zyli, xding, tliu}@ir.hit.edu.cn |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It describes processes in text and diagrams. |
| Open Source Code | Yes | All of our experiments were based on https://github.com/huggingface/pytorch-pretrained-BERT. We also released our code at https://github.com/eecrazy/TransBERT-ijcai2019. |
| Open Datasets | Yes | SCT v1.0 [Mostafazadeh et al., 2016] is the widely used version. ... SCT v1.5 [Sharma et al., 2018] is a recently released revised version... |
| Dataset Splits | Yes | Here we only use the development and test datasets, and split development set into 1,771 instances for training and 100 instances for development purposes. ... The detailed dataset statistics are shown in Table 1. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications, used for running the experiments. |
| Software Dependencies | No | The paper states: 'All of our experiments were based on https://github.com/huggingface/pytorch-pretrained-BERT.' While it mentions a library, it does not provide a specific version number for it or any other software component. |
| Experiment Setup | Yes | We train each transfer task and the SCT with 3 epochs monitoring on the development set, using a cross-entropy objective2. Other hyper parameters follow [Devlin et al., 2018]. |