A Primal Dual Formulation For Deep Learning With Constraints
Authors: Yatin Nandwani, Abhishek Pathak, Mausam, Parag Singla
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on the tasks of Semantic Role Labeling (SRL), Named Entity Recognition (NER) tagging, and fine-grained entity typing and show that our constraints not only significantly reduce the number of constraint violations, but can also result in state-of-the-art performance. |
| Researcher Affiliation | Academia | Yatin Nandwani, Abhishek Pathak, Mausam and Parag Singla Department of Computer Science and Engineering Indian Institute of Technology Delhi |
| Pseudocode | Yes | Algorithm 1 presents the pseudocode for our learning algorithm. |
| Open Source Code | Yes | We have made our all our code publicly available at: https://github.com/dair-iitd/dl-with-constraints for future research. |
| Open Datasets | Yes | We use English Ontonotes 5.0 dataset1 using the CONLL 2011/12 shared task format (Pradhan et al. [2012]) as the training data. 1http://cemantix.org/data/ontonotes.html We use the publicly available GMB4 dataset (Bos et al. [2017]) in our experiments. 4https://gmb.let.rug.nl/data.php We work with Typenet5 (Murty et al. [2017]), a publicly available dataset of hierarchical entity types for extremely fine-grained entity typing. 5https://github.com/iesl/Type Net |
| Dataset Splits | Yes | We use the standard train/dev/test split and use the official Perl script to compute span based F1-scores. We randomly split it into 60/20/20 train/dev/test sets respectively. We use the original splits of 90%, 5% and 5% for training, validation and testing, respectively (Murty et al. [2018]). |
| Hardware Specification | No | The paper mentions "IIT Delhi HPC facility" for computational resources but does not provide specific hardware details such as GPU models, CPU specifications, or memory sizes used for the experiments. |
| Software Dependencies | No | The paper mentions "implemented in https://allennlp.org/models#semantic-role-labeling" and refers to "software environments" in the supplement but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | No | The paper states: "The specific details of software environments and hyperparameters are mentioned in the supplement." However, these details are not provided in the main text of the paper. |