Event Detection via Gated Multilingual Attention Mechanism
Authors: Jian Liu, Yubo Chen, Kang Liu, Jun Zhao
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted extensive experiments on the ACE 2005 benchmark. Experimental results show that our approach significantly outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Jian Liu,1,2 Yubo Chen,1 Kang Liu,1,2 Jun Zhao,1,2 1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2 University of Chinese Academy of Sciences, Beijing, 100049, China |
| Pseudocode | No | The paper describes the GMLATT framework and its components using text and equations, but it does not include a formal pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on the widely used ACE 2005 dataset. This corpus contains 599 documents annotated with 8 types and 33 subtypes of events. ... We use online machine translation service3 to obtain the parallel text in TARGET language. ... We concatenate the translated multilingual data with a 200k parallel English-Chinese corpus5 released by (Eisele and Chen 2010) to learn the alignments together. 3http://fanyi.baidu.com/, in our experiment 5http://opus.lingfil.uu.se/Multi UN.php |
| Dataset Splits | Yes | We use the same data separation as the previous works: 40 particular articles are used as the blind test set; 30 articles are used as the development set; and the remaining 529 articles are used for training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper mentions software tools like GIZA++ and machine learning components like GRU and Bi-GRU, but it does not specify any version numbers for these software dependencies or libraries. |
| Experiment Setup | No | The paper mentions using 'mini-batch stochastic gradient descent (SGD)' and a 'dropout layer' and 'Negative sampling' for training and optimization, but it does not provide specific hyperparameter values such as learning rate, batch size, or dropout rate. |