Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Predicting Concrete and Abstract Entities in Modern Poetry
Authors: Fiammetta Caccavale, Anders Søgaard858-864
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper considers the problem of teaching a neural language model to select poetic entities, based on local context windows. We do so by fine-tuning and evaluating language models on the poetry of American modernists, both on seen and unseen poets, and across a range of experimental designs. |
| Researcher Affiliation | Academia | Fiammetta Caccavale, Anders Søgaard University of Copenhagen Universitetsparken 1 DK-2100 Copenhagen |
| Pseudocode | No | The paper describes the model architecture and training details in text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We use a publicly available implementation for our baseline,2 and modify the same code for our system, which we make publicly available here.3 |
| Open Datasets | Yes | Our dataset was collected using the public API Poetry DB (Poetry DB ). The Wikipedia corpus dump used in this study is publicly available.4 https://corpus.byu.edu/wiki/. Accessed [10-02-2018]. |
| Dataset Splits | Yes | The dataset crawled from Poetry DB has been divided into 80% training, 10% development, and 10% test set. |
| Hardware Specification | No | The paper mentions the model size (17.3M parameters) and type (bidirectional LSTMs) but does not specify any hardware details like GPU models, CPU types, or memory used for experiments. |
| Software Dependencies | No | The paper mentions using a bidirectional LSTM, word2vec, and RMSprop, but it does not provide specific version numbers for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | Our architecture is the following: the first layer is an embedding layer... The bidirectional LSTM layer has 200 neurons... Training details: The back-propagation algorithm... is run for 10 iterations.The training batch size is set to 32, and the pre-training learning rate used is 0.001... The training batch size is set to 32, the number of iterations to 10, and the learning rate used is 0.0005. |