Knowledge Enhanced Event Causality Identification with Mention Masking Generalizations
Authors: Jian Liu, Yubo Chen, Jun Zhao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |