Active Learning for Black-Box Semantic Role Labeling with Neural Factors
Authors: Chenguang Wang, Laura Chiticariu, Yunyao Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate the effectiveness of both this new active learning framework and the neural query strategy model. |
| Researcher Affiliation | Industry | Chenguang Wang, Laura Chiticariu, Yunyao Li IBM Research Almaden chenguang.wang@ibm.com, {chiti, yunyaoli}@us.ibm.com |
| Pseudocode | Yes | Algorithm 1 ACTIVESRL : Active Learning for Black-box SRL Model. Input: Labeled training data Dl train, labeled test data Dl test, unlabeled data Du, minimum accuracy change threshold minδ, maximum number of iterations max Iter. Output: An SRL model L srl. |
| Open Source Code | No | The paper provides links to third-party SRL models (MATE and CLEAR) that it uses, but there is no explicit statement or link indicating that the authors' own code for ACTIVESRL or QUERYM is open-source or publicly available. |
| Open Datasets | Yes | Datasets We conduct all the experiments based on CoNLL2009 shared task for English [Hajiˇc et al., 2009]. |
| Dataset Splits | Yes | We split the training set into two equal portions: denoted as TRAIN and DEV, and denote the in-domain and out-of-domain test sets in the CoNLL-2009 shared task as TESTid and TESTod. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions models like Skip-gram, TransE, and ASGD, but it does not provide specific version numbers for any software libraries, programming languages (other than Python generally), or development environments used for implementation. |
| Experiment Setup | Yes | n is set as |DEV| / max Iter. We also empirically set minδ as 0.0001 and max Iter as 10 [Settles, 2010] in line 5 of Algorithm 1. The same parameter values apply to all the above methods. |