Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification
Authors: Hirokazu Kiyomaru, Sadao Kurohashi10921-10929
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments with crowdsourced gold data in Japanese and English and show that our method effectively learns volitionality and subject animacy without manually labeled data. |
| Researcher Affiliation | Academia | Graduate School of Informatics, Kyoto University Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan {kiyomaru, kuro}@nlp.ist.i.kyoto-u.ac.jp |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and crowdsourced gold data are available at https: //github.com/hkiyomaru/volcls. |
| Open Datasets | Yes | We used 30M documents in CC-100 as a raw corpus (Conneau et al. 2020; Wenzek et al. 2020). |
| Dataset Splits | Yes | Table 3: Statistics of our dataset. The number with + means that the events were randomly sampled from a larger set according to the size of smallest dataset, Dl vol. Split Label Japanese English Dl vol Train Volitional 31,812 47,926 Non-volitional 81,002 40,564 Dev Volitional 149 67 Non-volitional 233 92 Test Volitional 149 68 Non-volitional 233 93 |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only mentions using Pytorch for implementation. |
| Software Dependencies | No | The paper mentions software like KNP, spacy, BERTBASE, and Pytorch, but does not provide specific version numbers for these software components, which is required for reproducibility. |
| Experiment Setup | Yes | α was selected from {0.0, 0.01, 0.1, 1.0} for each of WR, SOC, and ADA. β was selected from {0.0, 0.1, 1.0}. We trained the model for three epochs with a batch size of 256. We used the Adam optimizer (Kingma and Ba 2015) with a learning rate of 3e-5, linear warmup of the learning rate over the first 10% steps, and linear decay of the learning rate. |