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..
Knowledge Enhanced Event Causality Identification with Mention Masking Generalizations
Authors: Jian Liu, Yubo Chen, Jun Zhao
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we evaluate our model on three benchmark datasets and show our model outperforms previous methods by a significant margin. Moreover, we perform 1) cross-topic adaptation, 2) exploiting unseen predicates, and 3) crosstask adaptation to evaluate the generalization ability of our model. Experimental results show that our model demonstrates a definite advantage over previous methods. |
| Researcher Affiliation | Academia | Jian Liu1,2 , Yubo Chen1,2 and Jun Zhao1,2 1 National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China 2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | In our implementations1, both the knowledge aware reasoner and the mention masking reasoner are implemented as BERT-Large architecture, which has 24layer, 1024-hidden, and 16-heads. We use CONCEPTNET 5.0 as the KB. Regarding hyper-parameters, the batch size is set as 10, and the learning rate is initialized as 5 10 5 with a linear decay. We also adopt a negative sampling rate of 0.5 for training, owing to the sparseness of positive examples. 1https://github.com/jianliu-ml/Event Causality Identification |
| Open Datasets | Yes | Our experiments are conducted on three benchmark datasets, including: a) Event Strory Line [Caselli and Vossen, 2017], which contains 258 documents in 22 topics, 5,334 events in total, and 1,770 of 7,805 event pairs are causally related; b) Causal-Time Bank [Mirza et al., 2014], which contains 184 documents, 6,813 events, and 318 of 7,608 event pairs are causally related. c) Event Causality [Do et al., 2011; Ning et al., 2018], which contains 25 documents, 1,134 events, and 414 of 887 event pairs are causally related. |
| Dataset Splits | Yes | For Event Story Line, where we use the last two topics as development set, and conduct a 5-fold cross-validation on the rest 20 topics, as suggested by [Gao et al., 2019]. For Causal-Time Bank, where we adopt two settings: 1) 10-fold crossvalidation (CV) as in [Mirza, 2014a]... |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory amounts used for running its experiments. It mentions 'BERT-Large architecture' but no underlying hardware. |
| Software Dependencies | Yes | In our implementations1, both the knowledge aware reasoner and the mention masking reasoner are implemented as BERT-Large architecture... We use CONCEPTNET 5.0 as the KB. |
| Experiment Setup | Yes | Regarding hyper-parameters, the batch size is set as 10, and the learning rate is initialized as 5 10 5 with a linear decay. We also adopt a negative sampling rate of 0.5 for training, owing to the sparseness of positive examples. |